Signal processing apparatus and method for reducing noise and interference in speech communication and speech recognition

[0001] The present invention relates to a system and method for speech communication and speech recognition. It further relates to signal processing methods which can be implemented in the system.

[0002] The present applicant's PCT application PCT/SG99/00119, the disclosure of which is incorporated herein by reference in its entirety, proposes a method of processing signals in which signals received from an array of sensors are subject to a first adaptive filter arranged to enhance a target signal, followed by a second adaptive filter arranged to suppress unwanted signals. The output of the second filter is converted into the frequency domain, and further digital processing is performed in that domain.

[0003] The present invention seeks to further enhance the system by incorporating a third adaptive filter in the system and uses a novel method for performing improved signal processing of audio signals that are suitable for speech communication and speech recognition.

[0022] FIG. 1 illustrates schematically the operation environment of a signal processing apparatus 5 of the described embodiment of the invention, shown in a simplified example of a room. A target sound signal “s” emitted from a source s′ in a known direction impinging on a sensor array, such as a microphone array 10 of the apparatus 5, is coupled with other unwanted signals namely interference signals u1, u2 from other sources A, B, reflections of these signals u1r, u2r and the target signal's own reflected signal sr. These unwanted signals cause interference and degrade the quality of the target signal “s” as received by the sensor array. The actual number of unwanted signals depends on the number of sources and room geometry but only three reflected (echo) paths and three direct paths are illustrated for simplicity of explanation. The sensor array 10 is connected to processing circuitry 20-60 and there will be a noise input q associated with the circuitry which further degrades the target signal.

[0023] An embodiment of signal processing apparatus 5 is shown in FIG. 2. The apparatus observes the environment with an array of four sensors such as a plurality of microphones 10a-10d. Target and noise/interference sound signals are coupled when impinging on each of the sensors. The signal received by each of the sensors is amplified by an amplifier 20a-d and converted to a digital bitstream using an analogue to digital converter 30a-d. The bit Streams are feed in parallel to a digital signal processing means such as a digital signal processor 40 to be processed digitally. The digital signal processor 40 provides an output signal to a digital to an analogue converter 50 which is fed to a line amplifier 60 to provide the final analogue output.

[0024] FIG. 3 shows the major functional blocks of the digital signal processor in more detail. The multiple input coupled signals are received by the four-channel microphone array 10a-10d, each of which forms a signal channel, with channel 10a being the reference channel. The received signals are passed to a receiver front end which provides the functions of amplifiers 20 and analogue to digital converters 30 in a single custom chip. The four channel digitized output signals are fed in parallel to the digital signal processor 40. The digital signal processor 40 comprises five sub-processors. They are (a) a Preliminary Signal Parameters Estimator and Decision Processor 42, (b) a Signal Adaptive Filter 44 which may be referred to as a first adaptive filter, (c) an Adaptive Interference and Noise Filter 46 which may be referred to as a second adaptive filter, (d) an Adaptive Interference, Noise Cancellation and Suppression Processor 48 and (e) an Adaptive Speech Signal Pre-processor 50 which may be referred to as a third adaptive filter. The basic signal flow is from processor 42, to filter 44, to filter 46, to processor 48 and to filter 50. These connections being represented by thick arrows in FIG. 3. The filtered signal Ŝ and S′ is output from filter 48 and processor 50 respectively. Decisions necessary for the operation of the processor 40 are generally made by processor 42 which receives information from filters 44, 46, processor 48 and filter 50, makes decisions on the basis of that information and sends instructions to filters 44, 46, processor 48 and filter 50, through connections represented by thin arrows in FIG. 3. The outputs S′ and I of the processor 40 are transmitted to a Speech recognition engine 52.

[0025] It will be appreciated that the splitting of the processor 40 into five different modules 42, 44, 46, 48 and 50 is essentially notional and is mainly to assist understanding of the operation of the processor. The processor 40 would in reality be embodied as a single multi-function digital processor performing the functions described under control of a program with suitable memory and other peripherals. Furthermore, the operation of the speech recognition engine 52 could also be incorporated into the operation of the digital signal processor 40.

[0026] A flowchart illustrating the operation of the processors is shown in FIG. 4a-g and this will firstly be described generally. A more detailed explanation of aspects of the processor operation will then follow.

[0027] Referring to FIG. 4A, the method 400 of operation of the digital signal processor 40 starts with the step 405 of initializing and estimating parameters. Signals received from the microphone array 10a-d will be sampled and processed. Various energy and noise levels will also need to be estimated for further calculations in later steps.

[0028] Next, the step 410 is performed where direction of arrival of received signals at the microphone array 10a-d is determined and the presence of target signal is also tested for. Furthermore, in the same step 410, the received signals are processed by the Signal Adaptive Spatial Filter where an identified target signal is further enhanced.

[0029] Following which step 420 is carried out where the signal from the Signal Adaptive Spatial Filter is rechecked and filter coefficients reconfirmed.

[0030] In step 425, non-target signals, interference signals and noise signals are tested for and transformed into the frequency domain. In the same step, signals other than non-target signals, interference signals and noise signals are also transformed into the frequency domain.

[0031] The transformed signals then undergo step 430 where processing is performed by the Adaptive Interference and Noise Filter and the signals wrapped into Bark Scale.

[0032] After which step 440 is carried out where unvoice signals are detected and recovered and Adaptive Noise suppression is performed. In the same step, high frequency recovery by Adaptive Signal Fusion is also performed. The resulting signal is reconstructed in the time domain by an inverse wavelet transform.

[0033] Referring to FIG. 4B, the step 405 further comprises and starts with step 500 where a block of N/2 new signal samples are collected for all channels. The front end 20a-d, 30 processes samples of the signals received from array 10a-d at a predetermined sampling frequency, for example 16 kHz. The processor 42 includes an input buffer 43 that can hold N such samples for each of the four channels such that upon completion of step 500, the buffer holds a block of N/2 new samples and a block of N/2 previous samples.

[0034] The processor 42 then removes any DC from the new samples and pre-emphasizes or whitens the samples at step 502.

[0035] Following this, the total non-linear energy of a signal sample E_r1 and the average power of the same signal sample P_r1 are calculated at step 504. The samples from the reference channel 10a are used for this purpose although any other channel could be used. The samples are then transformed to 2 sub-bands through a Discrete Wavelet Transform at step 505. These 2 sub-bands may then be used later in step 440 for high frequency recovery.

[0036] From step 504, the system follows a short initialization period at step 506 in which the first 20 blocks of N/2 samples of a signal after start-up are used to estimate the environment noise energy and power level N_tge and N_ae respectively. Then, the samples are also used to estimate a Bark Scale system noise B_n at step 515. During this short period, an assumption is made that no target signals are present. B_n is then moved to point F to be used for updating B_y.

[0037] At step 508, it is determined if the signal energy E_r1 is greater than the noise threshold, T_tge1 and the signal power P_r1 is greater than the noise threshold, T_ae. If not, a new set of environment noise, N_tge, N_ae and B_n will be estimated.

[0038] During abrupt change of environment noise of present of target signal, signal energy E_r1 and the signal power P_r1 might be greater than their respective noise threshold. To differentiate between these two conditions, a further test is carried out at step 509. If the signal is from C′ (interference signal) and the energy ration R_sd is below 0.35 or the probability of speech present PB_Speech is below 0.25, these mean there is no target signal present in the signal and it is either interference of environment noise. Hence, the signal will move to step 515 where the system noise B_n is updated. Else, the signal passes to step 510.

[0039] At step 510 the signal to noise power ratio P_rsd and the environment noise energy level are used to estimate the dynamic noise power level, N_Prsd. This dynamic noise power level will track the system SNR level closely and in turn used for updating T_Rsd and T_Prsd. This close tracking of system SNR level will enable the system to detect target signal accurately during low SNR condition as show in FIG. 9.

[0040] Next, the updated noise energy level N_tge is used to estimate the 2 noise energy thresholds, T_tge1 and T_tge2. The updated noise power level N_ae is used to estimate the noise power threshold, T_ae at stage 512.

[0041] After this initialization period, N_tge, N_ae and B_n are updated when the update condition are fulfilled. As a result, the noise level threshold, T_tge1 and T_tge2 will be updated based on the previous N_tge, N_ae and B_n. This case T_tge1 and T_tge2 will follow the environment noise level closely. This is illustrated in FIG. 5 in which a signal noise level rises gradually from an initial level to a new level which both thresholds are still follow.

[0042] The apparatus only wishes to process candidate target signals that impinge on the array 10 from a known direction normal to the array, hereinafter referred to as the boresight direction, or from a limited angular departure there from, in this embodiment plus or minus 15 degrees. Therefore, the next stage is to check for any signal arriving from this direction.

[0043] Referring to FIG. 4C, the step 410 further starts with step 516, where three coefficients are established, namely a correlation coefficient C_x, a correlation time delay T_d and a filter coefficient peak ratio P_k. These three coefficients together provide an indication of the direction from which the target signal arrives from.

[0044] If at step 518, the estimated energy E_r1 in the reference channel 10a is found not to exceed the second threshold T_tge2, the target signal is considered not to be present and the method passes to step 530 for Non-Adaptive Filtering via steps 522-526 in which a counter C_L is incremented at step 522. At step 524, C_L is checked against a threshold T_CL. If the threshold is reached, block leaky is performed on the filter coefficient W_td at step 526 and counter C_L is also reset in the same step 526. This block leaky step improves the adaptation speed of the filter coefficient W_td to the direction of fast changing target sources and environment. At step 524, if the threshold is not reached, the method passes to step 530.

[0045] At step 518, if the estimated energy E_r1 is larger than threshold T_tge2, counter C_L is reset at step 519 and the signal will go through further verification at step 520 where four conditions are used to determine if the candidate target signal is an actual target signal. Firstly, the cross correlation coefficient C_x must exceed a predetermined threshold T_c. Secondly, the size of the delay coefficient T_d must be less than a value θ indicating that the signal has impinged on the array within a predetermined angular range. Thirdly the filter coefficient peak ratio P_k must be more than a predetermined threshold T_Pk1 and fourthly the dynamic noise power level, N_Prsd must be more that 0.5. If any one of these conditions is not met, the signal is not regarded as a target signal and the method passes to step 530 (non-target signal filtering). If all the conditions are met, the confirmed target signal undergoes step 528 where Adaptive Filtering (target signal filtering) by the Signal Adaptive Spatial Filter 44 takes place.

[0046] The Adaptive Spatial Filter 44 is instructed to perform adaptive filtering at step 528 and 532, in which the filter coefficients W_su are adapted to provide a “target signal plus noise” signal in the reference channel and “noise only” signals in the remaining channels using the Least Mean Square (LMS) algorithm. The filter 44 output channel equivalent to the reference channel is for convenience referred to as the Sum Channel and the filter 44 output from the other channels, Difference Channels. The signal so processed will be, for convenience, referred to as A′.

[0047] If the signal is considered to be a noise or interference signal, the method passes to step 530 in which the signals are passed through filter 44 without the filter coefficients being adapted, to form the Sum and Difference channel signals. The signals so processed will be referred to for convenience as B′.

[0048] The effect of the filter 44 is to enhance the signal if this is identified as a target signal but not otherwise.

[0049] Referring to FIG. 4D, the step of 420 further starts at step 534, if the signal is A′ signals from step 528 the method passes to step 536 where a new filter coefficient peak ratio P_k2 is calculated base on the filter coefficient W_su. This peak ratio is then compared with a best peak ratio BP_k at step 538. If it is larger than best peak ratio, the value of best peak ratio is replaced by this new peak ratio P_k2 with a forgetting factor of 0.95 and all the filter coefficients W_su are stored as the best filter coefficients at step 542. If it is not, the peak ratio P_k2 is again compared with a threshold T_Pk at step 544. If the peak ratio is below the threshold, a wrong update on the filter coefficients is deemed to have occurred and the filter coefficients are restored with the previous stored best filter coefficients. If it is above the threshold, the method passes to step 548.

[0050] If the signal from step 528 is not A′, the method passes from step 534 to step 548 where an energy ratio R_sd and power ratio P_rsd between the Sum Channel and the Difference Channels are estimated by processor 42. Following this, the adaptive noise power threshold T_Prsd, noise energy threshold T_Rsd and the maximum dynamic noise power threshold T_Prsd—max are updated base on the calculated power ratio P_rsd and N_Prsd.

[0051] Referring to FIG. 4E, the step of 421 further starts with the step 552 to determine the presence noise or interference. At step 552, six conditions are tested. Firstly, whether the signals are A′ signals from step 528. Secondly, whether the estimated energy E_r1 is less than the second threshold T_tge2, Thirdly, whether the cross correlation C_x is higher than a threshold T_c. If it is higher than threshold, this may indicate that there is a target signal. Fourthly, whether the delay coefficient T_d is less than a value θ, this may indicate that there is a target signal. Fifthly, whether the R_sd is higher than threshold T_rsd. Sixthly, whether P_rsd is higher than threshold T_Prsd. If the fifith and sixth condition are both higher than the respective thresholds, this may indicate that there has been some leakage of the target signal into the Difference channel, indicating the presence of a target signal after all.

[0052] Where any one of the six conditions are met, it is to be taken that target signals may well be present and the method then passes to step 556a.

[0053] Where all six conditions are not met, target signals are considered not present and the method passes to step 553 where a feedback factor, F_b is calculated before passes to step 554a. This feedback factor is implemented to adjust the amount of feedback based on noise level to obtain a balance among convergent rate, system stability and performance at adaptive interference and noise filter 46.

[0054] Before passed to step 556 or 554, these signals are collected for the new N/2 samples and the last N/2 samples from the previous block and a Hanning Window H_n is applied to the collected samples as shown in FIG. 13 to form vectors S_h, D_1h, D_2h, and D_3h. This is an overlapping technique with overlapping vectors S_h, D_1h, D_2h, and D_3h being formed from pass and present blocks of N/2 samples continuously. This is illustrated in FIG. 14. A Fast Fourier Transform is then performed on the vectors S_h, D_1h, D_2h, and D_3h to transform the vectors into frequency domain equivalents S_cf, D_1f, D_2f, and D_3f at step 554a and 556a respectively.

[0055] At step 554-558, the frequency domain signals S_cf, D_1f, D_2f, and D_3f are processed by the Adaptive Interference and Noise Filter 46 using a novel frequency domain Least Mean Square (FLMS) algorithm, the purpose of which is to reduce the unwanted signals. The filter 46, at step 554 is instructed to perform adaptive filtering on the non-target signals with the intention of adapting the filter coefficients to reducing the unwanted signal in the Sum channel to some small error value E_f at step 558. This computed E_f is also fed back to step 554 to calculate the adaptation rate of weight updating μ of each frequency beam. This will effectively prevent signal cancellation cause by wrong updating of filter coefficients. The signals so processed will be referred to for convenience as C′.

[0056] In the alternative, at step 556, the target signals are fed to the filter 46 but this time, no adaptive filtering takes place, so the Sum and Difference signals pass through the filter.

[0057] The output signals from processor 46 are thus the Sum channel signal S_cf, error output signal E_f at step 558 and filtered Difference signal S_i.

[0058] Referring to FIG. 4F, the step 430 further comprises and starts with calculating G_N, G_E and G. Next, step 562 is performed where, output signals from processor 46: S_cf, E_f and S_i are combined by adaptive weighted average G_N, G_E and G calculated at step 560 to produce a best combination signals S_f and I_f that optimize the signal quality and interference cancellation.

[0059] At step 564, a modified spectrum is calculated for the transformed signals to provide “pseudo” spectrum values P_s and P_i. P_s and P_i are then warped into the same Bark Frequency Scale to provide Bark Frequency scaled values B_s and B_i at step 566. With these two values, a probability of speech present, PB_Speech is calculated at step 567.

[0060] Referring to FIG. 4G, the step 440 further comprises and starts with step 568 where voice unvoice detection is performed on B_s and B_i from step 566 to reduce the signal cancellation on the unvoice signal.

[0061] A weighted combination B_y of B_n (through path E) and B_i is then made at step 570 and this is combined with B_s to compute the Bark Scale non-linear gain G_b at step 572.

[0062] G_b is then unwrapped to the normal frequency domain to provide a gain value G at step 574 and this is then used at step 576 to compute an output spectrum S_out using the signal spectrum S_f from step 562. This gain-adjusted spectrum suppresses the interference signals, the ambient noise and system noise.

[0063] An inverse FFT is then performed on the spectrum S_out at step 578 and the time domain signal is then reconstructed from the overlapping signals using the overlap add procedure at step 580. This time domain signal if subject to further high frequency recovery at step 581 where the signal are transform to two sub-bands at wavelet domain and multiplex with a reference signal. This multiplex signal is then reconstructed to time domain output signal, Ŝ_t by an inverse wavelet transform using the 2 sub-bands from the Discrete Wavelet Transform at step 505.

[0064] The method at this stage had essentially completed the noise suppression of the signals received earlier from the microphone array 10a-d. The resulting recovered Ŝ_t signal may be used readily for voice communication free from noise and interference in a variety of communication system and devices.

[0065] However, for this Ŝ_t signal to be further used for Speech Recognition purposes, further processing is required to assist the Speech Recognition Engine 52 from triggering when non-speech signals are received.

[0066] The Ŝ_t signal is further sent to the Speech Signal Pre-Processor 50 where an additional step 450 is performed for the pre-processing of the speech signal.

[0067] Referring to FIG. 4H, the step 450 further comprises step 582-598, where output signal Ŝ_t from Adaptive Interference and Noise Cancellation and Suppression Processor 48 was subjected to further processing before feeding to the Speech Recognition Engine 52 to reduce the frequency of false triggering. According to the value of continuous interference parameter P_ci and the status of continuous intermittent status parameter P_i, which were derived based on information gathered from the various stages of the microphone array processing algorithm, and counter Cnt_out, a decision is made on whether the signal Ŝ_t should be processed by a whitening filter.

[0068] Value of continuous interference threshold parameter P_TH, P_ci and the status of P_i are computed at step 582. If the signal current being processed contained the desired speech signal, program flows through the sequential steps 584, 586, 588, 590 or 584,586, 588 depending on the value of counter Cnter which is verified at step 588. Both of these sequences will not result in any modification to the signal Ŝ_t. Program flows through sequential steps 584, 592, 596 otherwise. The use of counter Cnt_out and Cnter has been a strategy adopted to protect the ending segment of desired speech signal. During this ending segment of speech, which is of small magnitude, parameters P_ci and P_i tend to be unreliable. This situation is especially true under loud interferences from the sides of the array. The counter Cnter is used to count the number of consecutive buffers which return false for the status of the Boolean expression P_ci<P_TH OR P_i=1 at step 584, a condition that is encountered in the presence of a desired speech segment. When Cnter reaches a pre-specified value, which is equal to 20 in this embodiment, it indicates that the algorithm is potentially processing a desired speech signal segment currently, the algorithm then sets the counter Cnt_out equal to a fixed value which correspond to the number of buffers to be output in the first instance when status of the Boolean expression P_ci<P_TH OR P_i=1 returns true.

[0069] At step 592, if the counter Cnt_out is greater than 0, condition indicating that the current buffer is likely to be the ending segment of a desired speech signal, Ŝ_t will bypass the whitening filter at step 596 and proceeds to step 594 that decrements counter Cnt_out by 1 and as well as resetting counter Cnter to 0. Again, this program sequence does not result in any modification to the signal Ŝ_t.

[0070] Program flows to step 596 if the counter Cnt_out is less than or equal 0 at step 592, this flow sequence, which only occur when the current buffer contains neither the desired speech signal nor the ending segment, results in the whitening of the signal Ŝ_t by the whitening filter and produce a clean output signal S′.

[0071] Besides providing the Speech Recognition Engine 52 with a processed signal S′, the system also provides a set of useful information indicated as I on FIG. 3. This set of information may include any one or more of:

• [0072] 1. Probability of Speech Present, PB_Speech (step 567)
• [0073] 2. The direction of speech signal, T_d (step 516)
• [0074] 3. Signal Energy, E_r1 (step 504)
• [0075] 4. Noise threshold, T_tge1 & T_tge2 (step 512)
• [0076] 5. Estimated SINR (signal to interference noise ratio) and SNR (signal to noise ratio), and R_sd (step 548)
• [0077] 6. Spectrum of processed speech signal, S_out (step 576)
• [0078] 7. Potential speech start and end point
• [0079] 8. Interference signal spectrum, I_f (step 562).

[0080] Major steps in the above described flowchart will now be described in more detail.

[0081] The processor 42 estimates the energy output from a reference channel. In the four channel example described, channel 10a is used as the reference channel.

[0082] N/2 samples of the digitized signal are buffered into a shift register to form a signal vector of the following form:

[0083] $\begin{array}{cc}{X}_r=\left[\begin{array}{c}{x}_r\\ x_r\left(2\right)\\ ⋮\\ x_r\left(J\right)\end{array}\right]& C\mathrm{.1}\end{array}$

[0084] Where J=N/2. The size of the vector depends on the resolution requirement. In the preferred embodiment, J=128 samples.

[0085] The nonlinear energy of the vector is then estimated using the following equation:

[0086] $\begin{array}{cc}{E}_{\mathrm{r1}}=\frac{1}{J-2}\sum _{i=1}^{J-2}x{\left(i\right)}^2-x\left(i+1\right)x\left(i-1\right)& A\mathrm{.1}\end{array}$

[0087] This Noise Level Estimation function is able to distinguish between speech target signal and environment noise signal. In this case the environment noise level can be track more closely and this means than the user can use the embodiment in all environments, especially noisy environments (car, supermarket, etc).

[0088] During system initialization, this Noise Level N_tge and N_ae are first established and the noise level threshold, T_tge1 and T_ae are then updated. N_tge and N_ae will continue to be updated when there is no target speech signal and the noise signal power E_r1 and P_r1 is less than the noise level threshold, T_tge1 and T_ae respectively.

[0089] A Bark Spectrum of the system noise and environment noise is also similarly computed and is denoted as B_n.

[0090] The noise level N_tge, N_ae and B_n are updated as follows:

[0091] If the signal energy of the reference signal is less than threshold, T_tge1 and the average power of the reference signal is less than threshold, T_ae or during the first 20 cycles of system initialization then, if the signal energy of the reference signal is less than the noise level N_tge,
α₁=0.98

[0092] Else
α₁=0.9
N_tge=α₁*N_tge+(1−α₁)*E_r1
N_ae=α₁*N_ae+(1−α₁)*P_r1
B_n=α₁*B_n+(1−α₁)*B_s

[0093] Where E_r1 is the signal energy of the reference signal and P_r1 is the average power of the reference signal.

[0094] Once the noise energy, N_tge and N_ae are obtained, the three noise threshold are established as follows:
T_tge1=β₁*N_tge
T_tge2=β₂*N_tge
T_ae=β₃*N_ae

[0095] In this embodiment, β₁=1.175, β₂=1.425 and β₃=1.3 have been found to give good results.

[0096] If there is an abrupt change in environment noise, the signal energy of the reference signal might be higher than threshold, T_tge1 and causes the B_n not updated. To overcome this, a condition is checked to make sure the estimated noise spectrum B_n is updated during this condition and whenever there is no target signal present. The updating condition is as follows:

[0097] If C′ and Rsd<0.35 or PB_Speech<0.25 then,
α₁=0.98
B_n=α₁*B_n+(1−α₁)*B_s

[0098] This dynamic noise power level, N_Prsd is estimated based on the signal power ratio Prsd and the environment noise level. It will then be used to update the dynamic noise power threshold, for this case T_Rsd, T_Prsd—max and T_Prsd. It is used to track closely the dynamic changing of the signal power ratio, P_rsd during no target signal present. A target signal is detected when the signal power ratio, P_rsd is higher than the dynamic noise power threshold, T_Prsd.

[0099] During noisy environment or low SNR condition, the signal power ratio, P_rsd will decrease to a lower level. In this case the dynamic noise power level, N_Prsd will follow the signal power ratio to that lower level. The dynamic noise power threshold, T_Prsd will also be set at a lower threshold. This will ensure any low SNR target signal to be detected because the signal power ratio, P_rsd of such target signal will also be lower. This is illustrated in FIG. 9.

[0100] This dynamic noise power level, N_Prsd is updated base on the following conditions: If the reference channel signal energy is less than T_tge1 and T_tge2 and power ratio is greater than 0.55 for 15 consecutive processing blocks,
N_Prsd=α₁*N_Prsd+(1−α₁)*β₁

[0101] Else if the reference channel signal energy is greater than T_tge1 and power ratio is less than 0.6 for 25 consecutive processing blocks,
N_Prsd=α₂*N_Prsd+(1−α₂)*T_Prsd—max

[0102] In this embodiment, α₁=0.7, α₂=0.85 and β₁=1.2 have been found to give good results.

[0103] FIG. 6A illustrates a single wave front impinging on the sensor array. The wave front impinges on sensor 10d first (A as shown) and at a later time impinges on sensor 10a (A′ as shown), after a time delay t_d. This is because the signal originates at an angle of 40 degrees from the boresight direction. If the signal originated from the boresight direction, the time delay t_d will have been zero ideally.

[0104] Time delay estimation of performed using a tapped delay line time delay estimator included in the processor 42 which is shown in FIG. 6B. The filter has a delay element 600, having a delay Z^−L/2, connected to the reference channel 10a and a tapped delay line filter 610 having a filter coefficient W_td connected to channel 10d. Delay element 600 provides a delay equal to half of that of the tapped delay line filter 610. The outputs from the delay element is d(k) and from filter 610 is d′(k). The Difference of these outputs is taken at element 620 providing an error signal e(k) (where k is a time index used for ease of illustration). The error is fed back to the filter 610. The Least Mean Squares (LMS) algorithm is used to adapt the filter coefficient W_td as follows:
W_td(k+1)=W_td(k)+2μ_tdS_10d(k)e(k)  B.1

[0105] $\begin{array}{cc}{W}_{\mathrm{td}}\left(k+1\right)=\left[\begin{array}{c}{W}_{\mathrm{td}}^0\left(k+1\right)\\ W_{\mathrm{td}}^1\left(k+1\right)\\ ⋮\\ W_{\mathrm{td}}^{\mathrm{L0}}\left(k+1\right)\end{array}\right]& B\mathrm{.2}\\ S_{10d}\left(k\right)=\left[\begin{array}{c}{S}_{10d}^0\left(k\right)\\ S_{10d}^1\left(k\right)\\ ⋮\\ S_{10d}^{\mathrm{L0}}\left(k\right)\end{array}\right]& B\mathrm{.3}\\ e\left(k\right)=d\left(k\right)-d^{\prime }\left(k\right)& B\mathrm{.4}\\ d^{\prime }\left(k\right)={W_{\mathrm{td}}\left(k\right)}^T·S_{10d}\left(k\right)& B\mathrm{.5}\\ {\mu }_{\mathrm{td}}=\frac{{\beta }_{\mathrm{td}}}{S_{10d}\left(k\right)}& B\mathrm{.6}\end{array}$
where β_td is a user selected convergence factor 0<β_td≦2, ∥ ∥ denoted the norm of a vector, k is a time index, L_o is the filter length.

[0106] The impulse response of the tapped delay line filter 620 at the end of the adaptation is shown in FIG. 6C. The impulse response is measured and the position of the peak or the maximum value of the impulse response relative to origin O gives the time delay T_d between the two sensors which is also the angle of arrival of the signal. In the case shown, the peak lies at the center indicating that the signal comes from the boresight direction (T_d=0). The threshold θ at step 506 is selected depending upon the assumed possible degree of departure from the boresight direction from which the target signal might come. In this embodiment, θ is equivalent to ±15°.

[0107] The normalized cross correlation between the reference channel 10a and the most distant channel 10d is calculated as follows:

[0108] Samples of the signals from the reference channel 10a and channel 10d are buffered into shift registers X and Y where X is of length J samples and Y is of length K samples, where J>K, to form two independent vectors X_r and Y_r:

[0109] $\begin{array}{cc}{X}_r=\left[\begin{array}{c}{x}_r\\ x_r\left(2\right)\\ ⋮\\ x_r\left(J\right)\end{array}\right]& C\mathrm{.1}\\ Y_r=\left[\begin{array}{c}{y}_r\\ y_r\left(2\right)\\ ⋮\\ y_r\left(K\right)\end{array}\right]& C\mathrm{.2}\end{array}$

[0110] A time delay between the signals is assumed, and to capture this Difference, J is made greater than K. The Difference is selected based on angle of interest. The normalized cross-correlation is then calculated as follows:

[0111] $\begin{array}{cc}{C}_x\left(l\right)=\frac{Y_r^T*X_{\mathrm{rl}}}{Y_r*X_{\mathrm{rl}}}& C\mathrm{.3}\\ \mathrm{Where}\dots X_{\mathrm{rl}}=\left[\begin{array}{c}{X}_r\\ X_r\left(l+1\right)\\ ⋮\\ x_r\left(K+l-1\right)\end{array}\right]& C\mathrm{.4}\end{array}$

[0112] Where ^T represents the transpose of the vector and ∥ ∥ represent the norm of the vector and l is the correlation lag. l is selected to span the delay of interest. For a sampling frequency of 16 kHz and spacing between sensors 10a, 10d of 18 cm, the lag l is selected to be five samples for an angle of interest of 15°.

[0113] The threshold T_c is determined empirically. T_c=0.65 is used in this embodiment.

[0114] The impulse response of the tapped delay line filter with filter coefficients W_td at the end of the adaptation with the presence of both signal and interference sources is shown in FIG. 7. The filter coefficient W_td is as follows:

[0115] $W_{\mathrm{td}}\left(k\right)=\left[\begin{array}{c}{W}_{\mathrm{td}}^0\left(k\right)\\ W_{\mathrm{td}}^1\left(k\right)\\ ⋮\\ W_{\mathrm{td}}^{\mathrm{L0}}\left(k\right)\end{array}\right]$

[0116] With the presence of both signal and interference sources, there will be more than one peak at the tapped delay line filter coefficient. The P_k ratio is calculated as follows:

[0117] $\begin{array}{cc}A=\mathrm{Max}{W}_{\mathrm{td}}^n& \mathrm{where}\frac{\mathrm{L0}}{2}-\Delta \le n\le \frac{\mathrm{L0}}{2}+\Delta \\ B={\mathrm{MaxpeakW}}_{\mathrm{td}}^n& \mathrm{where}0\le n<\frac{\mathrm{L0}}{2}-\Delta ,\frac{\mathrm{L0}}{2}+\Delta <n\end{array}$

[0118] $P_k=\frac{A}{A+B}$
Δ is calculated base on the threshold θ at step 530. In this embodiment, with θ equal to ±15°, Δ is equivalent to 2. A low P_k ratio indicates the present of strong interference signals over the target signal and a high P_k ratio shows high target signal to interference ratio.

[0119] Note that the value of B is obtained by scanning the maximum peak point at the two boundaries instead of taking the maximum point. This is to prevent a wrong estimation of P_k ratio when the center peak is broad and the high edge at the boundary B′ being misinterpreted as the value of B as shown in FIG. 8.

[0120] In the time delay estimation LMS algorithm, a modified leaky form is used. This is simply implemented by:
W_td=αW_td (where α=forgetting_factor˜=0.98)

[0121] This leaky form has the property of adapting faster to the direction of fast changing sources and environment.

[0122] FIG. 10 shows a block diagram of the Adaptive Linear Spatial Filter 44. The function of the filter is to separate the coupled target interference and noise signals into two types. The first, in a single output channel termed the Sum Channel, is an enhanced target signal having weakened interference and noise i.e. signals not from the target signal direction. The second, in the remaining channels termed Difference Channels, which in the four channel case comprise three separate outputs, aims to comprise interference and noise signals alone.

[0123] The objective is to adopt the filter coefficients of filter 44 in such a way so as to enhanced the target signal and output it in the Sum Channel and at the same time eliminate the target signal from the coupled signals and output them into the Difference Channels.

[0124] The adaptive filter elements in filter 44 acts as linear spatial prediction filters that predict the signal in the reference channel whenever the target signal is present. The filter stops adapting when the signal is deemed to be absent.

[0125] The filter coefficients are updated whenever the conditions of steps are met, namely:

• [0126] i. The adaptive threshold detector detects the presence of signal;
• [0127] ii The time delay estimation is within a certain threshold;
• [0128] iii The peak ratio exceeds a certain threshold;
• [0129] iv The cross correlation exceeds a certain threshold;
• [0130] v The dynamic noise power level exceed a certain threshold;

[0131] As illustrated in FIG. 10, the digitized coupled signal X₀ from sensor 10a is fed through a digital delay element 710 of delay Z^−Lsu/2. Digitized coupled signals X₁, X₂, X₃ from sensors 10b, 10c, 10d are fed to respective filter elements 712,4,6. The outputs from elements 710,2,4,6 are summed at Summing element 718, the output from the Summing element 718 being divided by four at the divider element 719 to form the Sum channel output signal. The output from delay element 710 is also subtracted from the outputs of the filters 712,4,6 at respective Difference elements 720,2,4, the output from each Difference element forming a respective Difference channel output signal, which is also fed back to the respective filter 712,4,6. The function of the delay element 710 is to time align the signal from the reference channel 10a with the output from the filters 712,4,6.

[0132] The filter elements 712,4,6 adapt in parallel using the normalized LMS algorithm given by Equations E.1 . . . E.8 below, the output of the Sum Channel being given by equation E.1 and the output from each Difference Channel being given by equation E.6:

[0133] $\begin{array}{cc}{\hat{S}}_c\left(k\right)=\frac{\stackrel{\_}{S}\left(k\right)+{\stackrel{\_}{X}}_0\left(k\right)}{4}& E\mathrm{.1}\\ \mathrm{Where}\text{:}\stackrel{\_}{S}\left(k\right)=\sum _{m=1}^{M-1}{\stackrel{\_}{S}}_m\left(k\right)& E\mathrm{.2}\\ {\stackrel{\_}{S}}_m\left(k\right)={\left(W_{\mathrm{su}}^m\left(k\right)\right)}^T{X}_m\left(k\right)& E\mathrm{.3}\end{array}$

[0134] Where m is 0,1,2 . . . M−1, the number of channels, in this case 0 . . . 3 and ^T denotes the transpose of a vector.

[0135] $\begin{array}{cc}{X}_m\left(k\right)=\left[\begin{array}{c}{X}_{1m}\left(k\right)\\ X_{2m}\left(k\right)\\ M\\ X_{\mathrm{LSUm}}\left(k\right)\end{array}\right]& E\mathrm{.4}\\ W_{\mathrm{su}}^m\left(k\right)=\left[\begin{array}{c}{W}_{\mathrm{su1}}^m\left(k\right)\\ W_{\mathrm{su2}}^m\left(k\right)\\ M\\ W_{\mathrm{suLSU}}^m\left(k\right)\end{array}\right]& E\mathrm{.5}\end{array}$

[0136] Where X_m(k) and W_su^m(k) are column vectors of dimension (Lsu×1).

[0137] The weight X_m(k) is updated using the normalized LMS algorithm as follows:
{circumflex over (∂)}_cm(k)= X₀(k)− S_m(k)  E.6
W_su^m(k+1)=W_su^m(k)+2μ_su^mX_m(k){circumflex over (∂)}_cm(k)  E.7

[0138] $\begin{array}{cc}\mathrm{Where}:{\mu }_{\mathrm{su}}^m=\frac{{\beta }_{\mathrm{su}}}{X_m\left(k\right)}& E\mathrm{.8}\end{array}$
and where β_su is a user selected convergence factor 0<β_su≦2, ∥ ∥ denoted the norm of a vector and k is a time index.

[0139] In the events of wrong updating of Spatial Filter, the coefficients of the filter could adapt to the wrong direction or sources. To reduce the effect, a set of ‘best coefficients’ is kept and copied to the beam-former coefficients when it is detected to be pointing to a wrong direction, after an update.

[0140] Two mechanisms are used for these:

[0141] A set of ‘best weight’ includes all of the three filter coefficients (W_su¹−W_su³). They are saved based on the following conditions:

[0142] When there is an update on filter coefficients W_su, the calculated P_k2 ratio is compared with the previous stored BP_k, if it is above the BP_k, this new set of filter coefficients shall become the new set of ‘best weight’ and current P_k2 ratio is saved as the new BP_k with a forgetting factor as follows:
BP_k=P_k2*α

[0143] In this embodiment the forgetting factor α is selected as 0.95 to prevent BP_k saturated and filter coefficient restore mechanism being locked.

[0144] A second mechanism is used to decide when the filter coefficients should be restored with the saved set of ‘best weights’. This is done when filter coefficients are updated and the calculated P_k2 ratio is below BP_k and threshold T_Pk. In this embodiment, the value of T_Pk is equal to 0.65.

[0145] This is performed as follows:

[0146] $\begin{array}{cc}{\hat{S}}_c=\left[\begin{array}{c}{\hat{S}}_c\left(0\right)\\ {\hat{S}}_c\left(1\right)\\ ⋮\\ {\hat{S}}_c\left(J-1\right)\end{array}\right]& F\mathrm{.1}\end{array}$

[0147] J=N/2, the number of samples, in this embodiment 256.

[0148] $\begin{array}{cc}{\hat{D}}_c=\left[\begin{array}{c}{\hat{\partial }}_c\left(0\right)\\ {\hat{\partial }}_c\left(1\right)\\ ⋮\\ {\hat{\partial }}_c\left(J-1\right)\end{array}\right]=\left[\begin{array}{c}{\hat{\partial }}_{\mathrm{c1}}\left(0\right)\\ {\hat{\partial }}_{\mathrm{c1}}\left(1\right)\\ ⋮\\ {\hat{\partial }}_{\mathrm{c1}}\left(J-1\right)\end{array}\right]+\left[\begin{array}{c}{\hat{\partial }}_{\mathrm{c2}}\left(0\right)\\ {\hat{\partial }}_{\mathrm{c2}}\left(1\right)\\ ⋮\\ {\hat{\partial }}_{\mathrm{c2}}\left(J-1\right)\end{array}\right]+& F\mathrm{.2}\\ \left[\begin{array}{c}{\hat{\partial }}_{\mathrm{c3}}\left(0\right)\\ {\hat{\partial }}_{\mathrm{c3}}\left(1\right)\\ ⋮\\ {\hat{\partial }}_{\mathrm{c3}}\left(J-1\right)\end{array}\right]& \\ E_{\mathrm{SUM}}=\frac{1}{J-2}\sum _{j=1}^{J-2}{{\hat{S}}_c\left(j\right)}^2-{\hat{S}}_c\left(j-1\right){\hat{S}}_c\left(j-1\right)& F\mathrm{.3}\\ E_{\mathrm{DIF}}=\frac{1}{3\left(J-2\right)}\sum _{j=1}^{J-2}{\hat{\partial }}_c{\left(j\right)}^2-{\hat{\partial }}_c\left(j-1\right){\hat{\partial }}_c\left(j-1\right)& F\mathrm{.4}\\ R_{\mathrm{sd}}=\frac{E_{\mathrm{SUM}}}{E_{\mathrm{DIF}}}& F\mathrm{.5}\end{array}$

[0149] Where E_SUM is the sum channel energy and E_DIF is the difference channel energy.

[0150] The energy ratio between the Sum Channel and Difference Channel (R_sd) must not exceed a dynamic threshold Trsd.

[0151] This is performed as follows:

[0152] $\begin{array}{c}{\hat{S}}_c=\left[\begin{array}{c}{\hat{S}}_c\left(0\right)\\ {\hat{S}}_c\left(1\right)\\ ⋮\\ {\hat{S}}_c\left(J-1\right)\end{array}\right]\\ {\hat{\partial }}_c=\left[\begin{array}{c}{\hat{\partial }}_c\left(0\right)\\ {\hat{\partial }}_c\left(1\right)\\ ⋮\\ {\hat{\partial }}_c\left(J-1\right)\end{array}\right]\\ =\left[\begin{array}{c}{\hat{\partial }}_{\mathrm{c1}}\left(0\right)\\ {\hat{\partial }}_{\mathrm{c1}}\left(1\right)\\ ⋮\\ {\hat{\partial }}_{\mathrm{c1}}\left(J-1\right)\end{array}\right]+\left[\begin{array}{c}{\hat{\partial }}_{\mathrm{c2}}\left(0\right)\\ {\hat{\partial }}_{\mathrm{c2}}\left(1\right)\\ ⋮\\ {\hat{\partial }}_{\mathrm{c2}}\left(J-1\right)\end{array}\right]+[\begin{array}{c}{\hat{\partial }}_{\mathrm{c3}}\left(0\right)\\ {\hat{\partial }}_{\mathrm{c3}}\left(1\right)\\ ⋮\\ {\hat{\partial }}_{\mathrm{c3}}\left(J-1\right)\end{array}]\end{array}$

[0153] J=N/2, the number of samples, in this embodiment 128.

[0154] Where P_SUM is the sum channel power and P_DIF is the difference channel power.

[0155] $\begin{array}{c}{P}_{\mathrm{SUM}}=\frac{1}{J}\sum _{j=0}^{J-1}{{\hat{S}}_c\left(j\right)}^2\\ P_{\mathrm{DIF}}=\frac{1}{3\left(J\right)}\sum _{j=0}^{J-1}{\hat{\partial }}_c{\left(j\right)}^2\\ P_{\mathrm{rsd}}=\frac{P_{\mathrm{SUM}}}{P_{\mathrm{DIF}}}\end{array}$

[0156] The power ratio between the Sum Channel and Difference Channel must not exceed a dynamic threshold, T_Prsd.

[0157] This dynamic noise energy threshold, T_Rsd is estimated based on the dynamic noise power level, N_Prsd. In this case T_Rsd will track closely with N_Prsd.

[0158] This dynamic noise energy threshold, T_Rsd is updated base on the following conditions:

If the dynamic noise power is more than 0.8,
T_Rsd=α₁*N_Prsd
Else
T_Rsd=α₂*N_Prsd
In this embodiment, α₁=1.7 and α₂=1.1 have been found to give good results. The maximum value of T_Rsd is set at 1.2 and the minimum value is set at 0.5.

[0159] This maximum dynamic noise power threshold, T_Prsd—max is estimated based on the dynamic noise power level, N_Prsd. It is used to determine the maximum noise power threshold for the dynamic noise power threshold, T_Prsd.

[0160] This maximum dynamic noise power threshold, T_Prsd—max is updated base on the following conditions:

If the dynamic noise power is more than 0.8,
T_Prsd—max=1.3
Else

[0161] If the reference channel signal energy is more than 1000
T_Prsd—max=α₁*N_Prsd
Else
T_Prsd—max=α₂*N_Prsd
In this embodiment, α₁=1.23 and α₂=1.45 have been found to give good results.

[0162] This dynamic noise power threshold, T_Prsd will track closely to the dynamic noise power level, N_Prsd and is updated base on the following conditions:

If the reference channel signal energy is more than 700 and power ratio is less than 0.45 for 64 consecutive processing blocks,
T_Prsd=α₁*T_Prsd+(1−α₁)*P_rsd
Else if the reference channel signal energy is less that 700, then
T_Prsd=α₂*T_Prsd+(1−α₂)*T_Prsd—max
In this embodiment, α₁=0.7 and α₂=0.98 have been found to give good results. The maximum value of T_Prsd is set at T_Prsd—max and the minimum value is set at 0.45.

[0163] Wrong updating or uncontrolled adaptation of interference filter coefficient during noisy and the presence of target signal can lead to signal cancellation and drastic performance degradation. On the other hand, an error feedback loop in filter coefficient updating will provide a more stable but slower convergent rate LMS. A feedback factor is implemented to adjust the amount of feedback based on noise level to obtain a balance among convergent rate, system stability and performance. This feedback factor is calculated as follows:
F_b=1−sfun(T_{Pr sd},0,1.5)
where sfun is a non-linear S-shape transfer function as shown in FIG. 11.

[0164] FIG. 12 shows a schematic block diagram of the Frequency Domain Adaptive Interference and Noise Filter 46. This filter adapts to noise and interference signal and subtracts it from the Sum Channel so as to derive an output with reduced interference noise in FFT domain.

[0165] In order to implement the well known overlap add block-processing technique, outputs from the Sum and Difference Channels of the filter 44 are buffered into a memory as illustrated in FIG. 13. The buffer consists of N/2 of new samples and N/2 of old samples from the previous block.

[0166] A Hanning Window is then applied to the N samples buffered signals as illustrated in FIG. 14 expressed mathematically as follows:

[0167] $\begin{array}{cc}{S}_h=\hspace{0.17em}\left[\begin{array}{c}{\hat{S}}_c\left(t+1\right)\\ {\hat{S}}_c\left(t+2\right)\\ M\\ {\hat{S}}_c\left(t+N\right)\end{array}\right]·H_n& \left(H\mathrm{.3}\right)\\ D_{\mathrm{mh}}=\left[\begin{array}{c}{\hat{\partial }}_{\mathrm{cm}}\left(t+1\right)\\ {\hat{\partial }}_{\mathrm{cm}}\left(t+2\right)\\ M\\ {\hat{\partial }}_{\mathrm{cm}}\left(t+N\right)\end{array}\right]·H_n& \left(H\mathrm{.4}\right)\end{array}$

[0168] Where (H_n) is a Hanning Window of dimension N, N being the dimension of the buffer. The “dot” denotes point-by-point multiplication of the vectors. t is a time index and m is 1,2 . . . M−1, the number of difference channels, in this case 1,2,3.

[0169] The resultant vectors [S_h] and [D_mh] are transformed into the frequency domain using Fast Fourier Transform algorithm as illustrated in equation H.6, H.7 and H.8 below:
S_cf=FFT(S_h)  (H.6)
D_mf=FFT(D_mh)  (H.7)

[0170] As illustrate at FIG. 12, the filter 46 takes D_1f, D_2f, and D_3f and feeds the Difference Channel Signals in parallel to a set of frequency domain adaptive filter elements 750,2,4. The outputs from the three filter elements 750,2,4 S_i are subtracted from the S_cf at Difference element 758 to form and error output E_f, which is fed back to the filter elements 750,2,4.

[0171] A modify block frequency domain Least Mean Square algorithm (FLMS) is used in this filter. This block frequency domain adaptive filter has faster convergent rate and less computational load as compared with time domain sliding window LMS algorithm use in PCT/SG99/00119. This frequency domain filter coefficients W_mf is adapt as follows:

[0172] $\begin{array}{cc}{E}_f\left(k\right)=S_{\mathrm{cf}}\left(k\right)-S_i\left(k\right)& \left(I\mathrm{.1}\right)\\ \mathrm{Where}{S}_i\left(k\right)=\frac{1}{M-1}\sum _{m=1}^{M-1}{Y}_{\mathrm{cm}}\left(k\right)Y_{\mathrm{cm}}\left(k\right)=D_{\mathrm{mf}}\left(k\right)W_{\mathrm{mf}}\left(k\right)& \left(I\mathrm{.2}\right)\end{array}$
D_mf(k)=diag{[D_m,1(k), . . . ,D_m,N(k)]^r}  (I.3)
W_mf(k)=[W_m,1(k), . . . W_m,N(k)]^r  (I.4)
W_mf(k+1)=W_mf(k)+2μ_m(k)D*_mf(k)E_f1(k)  (I.5)
μ_m(k)=β_uqdiag{P_m,1⁻¹(k), . . . ,P_m,N⁻¹(k)}  (I.6)
P_m,n(k)=F_b∥E_f,n(k)∥²+∥D_m,n(k)∥²  (I.7)
and where β_uq is a user select factor 0<β_uq≦2. m is 1,2 . . . M−1, the number of difference channels, in this case 1,2 and 3 and n is 1, . . . N, the block processing size. The ‘*’ denotes complex conjugate.

[0173] When target signal is presence and the Interference filter is updated wrongly, the error signal in equation I.1 will be very large. Hence, by including power of error signal ∥E_f∥² into weight updating μ calculation (equation I.6) of each frequency beam, the value of μ will become very small whenever there is a wrong updating of Interference filter occur. This form an error feedback loop which help to prevent a wrong updating of weight coefficients of Interference filter and hence reduce the effect of signal cancellation. F_b is the feedback factor determines the amount of feedback based on signal and noise level.

[0174] The output E_f from equation I.1 is almost interference and noise free in an ideal situation. However, in a realistic situation, this cannot be achieved. This will cause signal cancellation that degrades the target signal quality or noise or interference will feed through and this will lead to degradation of the output signal to noise and interference ratio. The signal cancellation problem is reduced in the described embodiment by use of the Adaptive Spatial Filter 44 which reduces the target signal leakage into the Difference Channel. However, in cases where the signal to noise and interference is very high, some target signal may still leak into these channels.

[0175] To further reduce the target signal cancellation problem and unwanted signal feed through to the output, the output signals from processor 46 are fed into the Adaptive NonLinear Interference and Noise Suppression Processor 48 as described below.

[0176] The frequency domain filter output (S_i), error output signal (E_f) and the Sum Channel output signal (S_cf) are combined as a weighted average as follows:
S_f=G_N*S_cf+G_E*E_f
I_f=G*S_i

[0177] The weights G, G_N and G_E are adaptively changing based on signal to noise and interference ratio to produce a best combination that optimize the signal quality and interference cancellation.

[0178] During quiet or low noise environment if a speech target signal is detected, G_E will decrease and G_N increase thus S_f will receive more speech target signals from the Signal Adaptive Spatial Filter (Filter 44). In this case the filtered signal and the non-filtered signal will be closely matched. For noisy environment when a speech target signal is detected, G_E will increase and G_N decrease, now S_f will receive more speech target signals from the Adaptive Interference Filter (Filter 46). Now the speech signal will be highly coupled with noise and this need to be filtered out. G will determine the amount of noise input signal.

[0179] G_new is chosen based on the lower and upper limit of the s-function on the Energy Ratio, R_sd. Depending of the update condition of the Signal Adaptive Spatial Filter and the Adaptive Interference Filter, the value of G, G_N and G_E are calculated and stored separately for each update condition. These stored values are used in the next cycle of computation. This will ensure a steady state value even if the update condition changes frequently.

[0180] This three Signal to Noise Ratio Gain G, G_N and G_E are updated base on the following conditions:

If the Signal Adaptive Spatial Filter is updated,
G₁=α₁*G₁+(1−α₁)*G_new
G_E1=α₁*G_E1+(1−α₁)*G₁
G_N1=α₁*G_N1+(1−α₁)*(1−G₁)
G=G₁
G_E=G_E1
G_N=G_N1
Else if the Adaptive Interference Filter is updated,
G₂=α₁*G₁+(1−α₁)*G_new
G_E2=α₁*G_E2+(1−α₁)*G₂
G_N2=α₁*G_N2+(1−α₁)*(1−G₂)
G=G₂
G_E=G_E2
G_N=G_N2
Else then,
G₃=α₁*G₃+(1−α₁)*G_new
G_E3=α₁*G_E3+(1−α₁)*G₃
G_N3=α₁*G_N3+(1−α₁)*(1−G₃)
G=G₃
G_E=G_E3
G_N=G_N3
In this embodiment, α₁=0.9 has been found to give good results.

[0181] A modified spectrum is then calculated, which is illustrated in Equations H.9 and H.10:
P_s=|Re(S_f)|+|Im(S_f)|+F(S_f)*r_s  (H.9)
P_i=|Re(I_f)|+|Im(I_f)|+F(I_f)*r_i  (H.10)

[0182] Where “Re” and “Im” refer to taking the absolute values of the real and imaginary parts, r_s and r_i are scalars and F(S_f) and F(I_f) denotes a function of S_f and I_f respectively.

[0183] One preferred function F using a power function is shown below in equation H.11 and H.12 where “Conj” denotes the complex conjugate:
P_s=|Re(S_f)|+|Im(S_f)|+(S_f*conj(S_f))*r_s  (H.11)
P_i=|Re(I_f)|+|Im(I_f)|+(I_f*conj(I_f))*r_i  (H.12)

[0184] A second preferred function F using a multiplication function is shown below in equations H.13 and H.14:
P_s=|Re(S_f)|+|Im(S_f)|+|Re(S_f)|*|Im(S_f)|*r_s  (H.13)
P_i=|Re(I_f)|+|Im(I_f)|+|Re(I_f)|*|Im(I_f)|*r_i  (H.14)

[0185] The values of the scalars (r_s and r_i) control the tradeoff between unwanted signal suppression and signal distortion and may be determined empirically. (r_s and r_i) are calculated as 1/(2^vs) and 1/(2^vi) where vs and vi are scalars. In this embodiment, vs=vi is chosen as 8 giving r_s=r_i=1/256. As vs and vi reduce, the amount of suppression will increase.

[0186] The Spectra (P_s) and (P_i) are warped into (Nb) critical bands using the Bark Frequency Scale [See Lawrence Rabiner and Bing Hwang Juang, Fundamental of Speech Recognition, Prentice Hall 1993]. The number of Bark critical bands depends on the sampling frequency used. For a sampling of 16 kHz, there will be Nb=22 critical bands. The warped Bark Spectrum of (P_s) and (P_i) are denoted as (B_s) and (B_i).

[0187] This probability of speech present is to give a good indication of whether target signal present at the input even the environment is very noisy and the SNR below 0 dB. It is calculated as follows:

[0188] $\mathrm{Sp}=\frac{P_s}{P_i+1}$ ${\mathrm{pbs}}_k\left(n\right)=\alpha *{\mathrm{pbs}}_{k-1}\left(n\right)+\left(1-\alpha \right)*\mathrm{Isp}$ $\mathrm{where}\{\begin{array}{cc}\mathrm{Isp}=1& \mathrm{if}\mathrm{Sp}\left(n\right)>2.5\\ \mathrm{Isp}=0& \mathrm{if}\mathrm{Sp}\left(n\right)\le 2.5\end{array}\mathrm{PB\_Speech}=\stackrel{\_}{\mathrm{pbs}}$
where, n=1 to Nb and α is used to adjust the rate of adaptation of the probability, in this embodiment α=0.2 give a good result. A high PB_Speech that closer to one indicate a high probability of target signal present at the input. Whereas, a low PB_Speech indicates the probability of target signal present at the input is low.

[0189] This is used to detect voice or unvoiced signal from the Bark critical bands of sum signal and hence reduce the effect of signal cancellation on the unvoiced signal. It is performed as follows:

[0190] $\begin{array}{c}{B}_s=\left[\begin{array}{c}{B}_s\left(0\right)\\ B_s\left(1\right)\\ ⋮\\ B_s\left(\mathrm{Nb}\right)\end{array}\right]\\ V_{\mathrm{sum}}=\sum _{n=0}^k{B}_s\left(n\right)\end{array}$
where k is the voice band upper cutoff

[0191] $U_{\mathrm{sum}}=\sum _{n=l}^{\mathrm{Nb}}{B}_s\left(n\right)$
where l is the unvoiced band lower cutoff

[0192] $\mathrm{Unvoice\_Ratio}=\frac{U_{\mathrm{sum}}}{V_{\mathrm{sum}}}$
If Unvoice_Ratio>Unvoice_Th
B_s(n)=B_s(n)×A

[0193] where l≦n≦Nb

In this embodiment, the value of voice band upper cutoff k, unvoiced band lower cutoff l, unvoiced threshold Unvoice_Th and amplification factor A is equal to 16, 18, 10 and 8 respectively.

[0194] A Bark Spectrum of the system noise and environment noise is similarly computed and is denoted as (B_n). B_n is first established during system initialization as B_n=B_s and continues to be updated when no target signal is detected by the system i.e. any silence period. B_n is updated as follows:

[0195] If the signal energy of the reference signal E_r1 is less than threshold, T_tge1 and the average power of the reference signal is less than threshold, T_ae or during the first 20 cycles of system initialization then,

[0196] If the signal energy of the reference signal is less than the noise level N_tge,
α=0.98
Else
α=0.9
B_n=α*B_n+(1−α)*B_s

[0197] Using (B_s, B_i and B_n) a non-linear technique is used to estimate a gain (G_b) as follows:

[0198] First the unwanted signal Bark Spectrum is combined with the system noise Bark Spectrum by using as appropriate weighting function as illustrate in Equation J.1.
B_y=Ω₁B_i+Ω₂B_n  (J.1)

[0199] Ω₁ and Ω₂ are weights whose can be chosen empirically so as to maximize unwanted signals and noise suppression with minimized signal distortion. In this embodiment, Ω₁=1.0 and Ω₂=0.25.

[0200] Follow that a post signal to noise ratio is calculated using Equation J.2 and J.3 below:

[0201] $\begin{array}{cc}{R}_{\mathrm{po}}=\frac{B_s}{B_y}& \left(J\mathrm{.2}\right)\\ R_{\mathrm{pp}}=R_{\mathrm{po}}-I_{\mathrm{Nbx1}}& \left(J\mathrm{.3}\right)\end{array}$

[0202] The division in equation J.2 means element-by-element division and not vector division. R_po and R_pp are column vectors of dimension (Nb×1), Nb being the dimension of the Bark Scale Critical Frequency Band and I_Nb×1 is a column unity vector of dimension (Nb×1) as shown below:

[0203] $\begin{array}{cc}{R}_{\mathrm{po}}=\left[\begin{array}{c}{r}_{\mathrm{po}}\left(1\right)\\ r_{\mathrm{po}}\left(2\right)\\ M\\ r_{\mathrm{po}}\left(\mathrm{Nb}\right)\end{array}\right]& \left(J\mathrm{.4}\right)\\ R_{\mathrm{pp}}=\left[\begin{array}{c}{r}_{\mathrm{pp}}\left(1\right)\\ r_{\mathrm{pp}}\left(2\right)\\ M\\ r_{\mathrm{pp}}\left(\mathrm{Nb}\right)\end{array}\right]& \left(J\mathrm{.5}\right)\\ I_{\mathrm{Nbx1}}=\left[\begin{array}{c}1\\ 1\\ M\\ 1\end{array}\right]& \left(J\mathrm{.6}\right)\end{array}$

[0204] If any of the r_pp elements of R_pp are less than zero, they are set equal to zero.

[0205] Using the Decision Direct Approach [see Y. Ephraim and D. Malah: Speech Enhancement Using Optimal Non-Linear Spectrum Amplitude Estimation; Proc. IEEE International Conference Acoustics Speech and Signal Processing (Boston) 1983, pp 1118-1121.], the a-priori signal to noise ratio R_pr is calculated as follows:

[0206] $\begin{array}{cc}{R}_{\mathrm{pr}}=\left(1-{\beta }_i\right)*R_{\mathrm{pp}}+{\beta }_i*\frac{B_o}{B_y}& \left(J\mathrm{.7}\right)\end{array}$
B_o/B_y  (J.7)

[0207] The division in Equation J.7 means element-by-element division. B_o is a column vector of dimension (Nb×1) and denotes the output signal Bark Scale Bark Spectrum from the previous block B_o=G_b×B_s (See Equation J.15) (B_o initially is zero). R_pr is also a column vector of dimension (Nb×1). The value of β_i is given in Table 1 below:

[0208]

	TABLE 1
	i
	1	2	3	4	5
	βi	0.01625	0.1225	0.245	0.49	0.98

[0209] The value i is set equal to 1 on the onset of a signal and β_i value is therefore equal to 0.01625. Then the i value will count from 1 to 5 on each new block of N/2 samples processed and stay at 5 until the signal is off. The i will start from 1 again at the next signal onset and the β_i is taken accordingly.

[0210] Instead of β_i being constant, in this embodiment β_i is made variable based on PB_Speech and starts at a small value at the onset of the signal to prevent suppression of the target signal and increases, preferably exponentially, to smooth R_pr.

[0211] From this, R_rr is calculated as follows:

[0212] $\begin{array}{cc}{R}_{\mathrm{rr}}=\frac{R_{\mathrm{pr}}}{I_{\mathrm{Nbx1}}+R_{\mathrm{pr}}}& \left(J\mathrm{.8}\right)\end{array}$

[0213] The division in Equation J.8 is again element-by-element. R_rr is a column vector of dimension (Nb×1).

[0214] From this, L_x is calculated:
L_x=R_rr·R_po  (J.9)

[0215] The value L_x of is limited to Pi (≈3.14). The multiplication is Equation J.9 means element-by-element multiplication. L_x is a column vector of dimension (Nb×1) as shown below:

[0216] $\begin{array}{cc}{L}_x=\left[\begin{array}{c}{l}_x\left(1\right)\\ l_x\left(2\right)\\ M\\ l_x\left(\mathrm{nb}\right)\\ M\\ l_x\left(\mathrm{Nb}\right)\end{array}\right]& \left(J\mathrm{.10}\right)\end{array}$

[0217] A vector L_y of dimension (Nb×1) is then defined as:

[0218] $\begin{array}{cc}{L}_y=\left[\begin{array}{c}{l}_y\left(1\right)\\ l_y\left(2\right)\\ M\\ l_y\left(\mathrm{nb}\right)\\ M\\ l_y\left(\mathrm{Nb}\right)\end{array}\right]& \left(J\mathrm{.11}\right)\end{array}$
Where nb=1,2 . . . Nb. Then L_y is given as:

[0219] $\begin{array}{cc}{l}_y\left(\mathrm{nb}\right)=\exp \left(\frac{E\left(\mathrm{nb}\right)}{2}\right)& \left(J\mathrm{.12}\right)\\ \mathrm{and}& \\ E\left(\mathrm{nb}\right)=-0.57722-\log \left(l_x\left(\mathrm{nb}\right)\right)+l_x\left(\mathrm{nb}\right)-\mathrm{ }\frac{{\left(l_x\left(\mathrm{nb}\right)\right)}^2}{4}+\frac{{\left(l_x\left(\mathrm{nb}\right)\right)}^3}{8}-\frac{{\left(l_x\left(\mathrm{nb}\right)\right)}^4}{96}K& \left(J\mathrm{.13}\right)\end{array}$
E(nb) is truncated to the desired accuracy. L_y can be obtained using a look-up table approach to reduce computational load.

[0220] Finally, the Gain G_b is calculated as follows:
G_b=R_rr·L_y  (J.14)

[0221] The “dot” again implies element-by-element multiplication. G_b is a column vector of dimension (Nb×1) as shown:

[0222] $\begin{array}{cc}{G}_b=\left[\begin{array}{c}g\left(1\right)\\ g\left(2\right)\\ M\\ g\left(\mathrm{nb}\right)\\ M\\ g\left(\mathrm{Nb}\right)\end{array}\right]& \left(J\mathrm{.15}\right)\end{array}$

[0223] As G_b is still in the Bark Frequency Scale, it is then unwrapped back to the normal linear frequency scale of N dimensions. The unwrapped G_b is denoted as G.

[0224] The output spectrum with unwanted signal suppression is given as:
S_f=G·S_f  (J.16)
The “·” again implies element-by-element multiplication.

[0225] The recovered time domain signal is given by:
S_t=Re(IFFT( S_f))  (J.17)
IFFT denotes an Inverse Fast Fourier Transform, with only the Real part of the inverse transform being taken.

[0226] The time domain signal is obtained by overlap add with the previous block of output signal:

[0227] $\begin{array}{cc}{S}_t=\left[\begin{array}{c}{\stackrel{\_}{S}}_t\left(1\right)\\ {\stackrel{\_}{S}}_t\left(1\right)\\ M\\ {\stackrel{\_}{S}}_t\left(N/2\right)\end{array}\right]+\left[\begin{array}{c}{Z}_t\left(1\right)\\ Z_t\left(1\right)\\ M\\ Z_t\left(N/2\right)\end{array}\right]& \left(J\mathrm{.18}\right)\\ \mathrm{Where}\text{:}{Z}_t=\left[\begin{array}{c}{\stackrel{\_}{S}}_{t-1}\left(1+N/2\right)\\ {\stackrel{\_}{S}}_{t-1}\left(2+N/2\right)\\ M\\ {\stackrel{\_}{S}}_{t-1}\left(N\right)\end{array}\right]& \left(J\mathrm{.19}\right)\end{array}$

[0228] This time domain signal is then multiplex with a reference channel signal in wavelet domain to recover any high frequency component that loss through out the processing.

[0229] A one level wavelet transform is performed on both the reference signal and the time domain output signal as follows:
[Zw_L Zw_H]=DWT(X_y)
[Zd_L Zd_H]=DWT(S_t)

[0230] where L=1:N/4, H=N/4+1:N/2 and DWT denote discrete wavelet transform.

[0231] Then the high frequency recovery is perform on the wavelet domain as follows:

[0232] If the signals are A′ signals from step 528
Zs_H=G_E*Zw_H+G_N*Zd_H
else
Zs_H=G_N*Zw_H+G_E*Zd_H

[0233] The final time domain output signal is then obtained by performing an inverse wavelet transform on the multiplex sub-bands as follows:
{circumflex over (S)}_t=IDWT[Zd_L Zs_H]

[0234] Although the interference and noise signals have been suppressed to a great deal by the Adaptive NonLinear Interference and Noise Suppression Processor, residual interference signals of small magnitude do exist at the output Ŝ_t. When this output is used to drive a speaker and be listened by a person, these residual interference signals were barely audible or intelligible and were thus ignored by the listener. However, when this output is fed to a speech recognition engine, the residual interference signals cause false triggering of the Speech Recognition Engine.

[0235] In order to reduce the frequency of false triggering, the Speech Signal Pre-processor was introduced to further process the output signal from the Adaptive Interference and Noise Cancellation and Suppression Processor.

[0236] FIG. 15 depicts the block diagram of the speech signal pre-processor. The pre-processor gathers information from the various stages of the processor 42-48 and compute the parameters: continuous interference parameter P_ci and intermittent interference status parameter P_i. Base on the value of P_ci. and counter Cnt_out and the status of P_i, a decision is made on whether the signal Ŝ_t should be processed by the Adaptive Whitening Filter.

[0237] Should P_ci be lower than dynamic continuous interference threshold P_TH, which is determined empirically, or the logic value of P_i is ‘1’ and together with the condition that the value of Cnt_out is less than 0, the input signal will be processed by the whitening filter. Otherwise, the input signal will simply bypass the whitening filter. In the whitening filter implementation, the Normalized Least Mean Square algorithm (NLMS) is used to adaptively adjust the coefficients of the tapped delay line filter.

[0238] The rationale for having two parameters has been that the P_i parameter is useful in situation where the interference from the side of the sensors is intermittent while P_ci is useful in situation where the interference is continuous. The use of counter Cnt_out has been a strategy adopted to protect the ending segment of desired speech signal. During this ending segment of speech, which is of small magnitude, parameters P_ci. and P_i tend to be unreliable. This situation is especially true under loud interferences from the sides of the sensors. A counter Cnter is used to count the number of consecutive buffers which return false for the status of the Boolean expression P_ci<P_TH OR P_i=1. When Cnter reached a pre-specified value, which is equal to 20 in this embodiment, it signify that the algorithm is currently processing a desired speech segment, the algorithm then set the counter Cnt_out equal to a fixed value which correspond to the number of buffers to be output in the first instance when status of the Boolean expression P_ci<P_TH OR P_i=1 return true.

[0239] For the dynamic continuous interference threshold P_TH, it is selected base on the following conditions:

[0240]

	If the TPrsd is less than 0.5,
	PTH = χ1
	Else
	PTH = χ2

Setting χ₁=0.05 and χ₂=0.143 have been able to produce good results.

[0241] The logic value of intermittent interference status parameter P_i is determined through the following conditions,

[0242]

	If abs(Td) is greater than δ1 and TPrsd is greater than δ2
	and Pk is less than δ3,
	Pi = 1
	Else
	Pi = 0

where abs( ) is taking the absolute value of its operand. In this embodiment, δ₁=2, δ₂=1.0 and δ₃=0.5 have been found to give good results.

[0243] In order to obtain a robust parameter to be used under varying interference scenarios, a number of parameters have been combined to create a new parameter. In this case, the suppression parameter is derived based on the weighted sum of three parameters given by the following equation:
P_ci=ε₁*P_{S{circumflex over (∂)}}+ε₂*P_wtpk+ε₃*P_micxcorr

[0244] Computation of signal to error ratio P_{S{circumflex over (∂)}}, normalized filter coefficient peak ratio P_wtpk and transformed normalized crossed correlation estimation P_micxcorr will follow in the next few sections. In this embodiment, ε₁=0.55, ε₂=0.35 and ε₃=0.1 have been found to give good results.

[0245] P_{S{circumflex over (∂)}} is computed by mapping the ratio of S_pow/{circumflex over (∂)}_c3—pow to a value of between 0 and 1 through the s-function. S_pow is the power of the output signal Ŝ_t from the Adaptive Interference and Noise Cancellation and Suppression Processor and {circumflex over (∂)}_c3—pow is the power of the signal on the last Difference Channel, {circumflex over (∂)}_c3 (k). In the computation, the lower limit of the s-function is set to 0 while the upper limit, L_u, changes dynamically based on the following linear equation,
L_u=9.1*T_Prsd−3.37

[0246] In addition, the range of variation is also limited to be in the range of between 1.0 and 3.0.

• • [0247] If L_u is less than 1.0,
    L_u=1.0
  • [0248] If L_u is greater than 3.0,
    L_u=3.0

[0249] The parameter P_wtpk is derived from the product of two parameters, namely P_wt and P_pk. P_wt is computed by applying the s-function to the ratio of A/∥W_td∥. Where A is defined as the maximum value of tapped delay line filter coefficients W_td within the index range of

[0250] $\frac{L0}{2}-\Delta \le n\le \frac{L0}{2}+\Delta ,$
where L0 is the filter length and Δ is calculated base on the threshold θ, with θ equal to ±15° in this embodiment, Δ is equivalent to 2. And ∥W_td∥ is the norm of the coefficients of the tapped delay line filter. P_pk is obtained by applying the s-function to the P_k parameter.

[0251] In this embodiment, the lower and upper limits used in the s-function for the computation of P_wt are 0.2 and 1.0 respectively. As for P_pk, the lower and upper limits used in the s-function are 0.05 and 0.55 respectively.

[0252] The parameter P_micxcorr is derived from the normalized cross correlation estimation C_x, which is the cross correlation between the reference channel 10a and the most distant channel 10d. P_micxcorr is computed by mapping C_x to a value of between 0 and 1 through the s-function. In this embodiment, the upper limit of the s-function is set to 1 and the lower limit is set to 0 for this particular computation.

[0253] The whitening of output time sequence Ŝ_t is achieved through a one step forward prediction error filter. The objective of whitening is to reduce instances of false triggering to the Speech Recognition Engine cause by the residual interference signal.

[0254] Denoting the Lsux1 observation vector as,

[0255] $X_{\mathrm{wh}}\left(k\right)=\left[\begin{array}{c}{\hat{S}}_t\left(k-1\right)\\ {\hat{S}}_t\left(k-2\right)\\ M\\ {\hat{S}}_t\left(k-\mathrm{LSU}\right)\end{array}\right]\mathrm{and}{W}_{\mathrm{wh}}\left(k\right)=\left[\begin{array}{c}{W}_1\left(k\right)\\ W_2\left(k\right)\\ M\\ W_{\mathrm{LSU}}\left(k\right)\end{array}\right]$
as the tap coefficients of the forward prediction error filter. The weight vector W_wh(k) is updated using the normalized LMS algorithm as follows:

[0256] Predicted value of X(k),
{circumflex over (X)}(k)=(W_wh(k))^TX_wh(k)

[0257] Forward prediction error,
S_wh(k)=X(k)−{circumflex over (X)}(k)

[0258] Adaptation step size,

[0259] ${\mu }_{\mathrm{wh}}\left(k\right)=\frac{{\beta }_{\mathrm{wh}}}{\sigma X_{\mathrm{wk}}\left(k\right)+\left(1-\sigma \right)S_{\mathrm{wh}}^2\left(k\right)}$

[0260] Tap-weight adaptation,
W_wh(k+1)=W_wh(k)+2μ_whX_wh(k)S_wh(k)

[0261] where ^T denotes the transpose of a vector, ∥ ∥ denotes the norm of a vector and β_wh is a user selected convergence factor 0<β_su≦2, and k is a time index. The adaptation step size μ_wh(k) is slightly varied from that of the conventional normalized LMS algorithm. An error term S_wh²(k) is included in this case to provide better control of the rate of adaptation as well. The value of σ is in the range of 0 to 1. In this embodiment, σ is equal to 0.1.

[0262] The embodiment described is not to be construed as limitative. For example, there can be any number of channels from two upwards. Furthermore, as will be apparent to one skilled in the art, many steps of the method employed are essentially discrete and may be employed independently of the other steps or in combination with some but not all of the other steps. For example, the adaptive filtering and the frequency domain processing may be performed independently of each other and the frequency domain processing steps such as the use of the modified spectrum, warping into the Bark scale and use of the scaling factor β_i can be viewed as a series of independent tools which need not all be used together.

[0263] Use of first, second etc. in the claims should only be construed as a means of identification of the integers of the claims, not of process step order. Any novel feature or combination of features disclosed is to be taken as forming an independent invention whether or not specifically claimed in the appendant claims of this application as initially filed.