Concealment of frame erasures for speech transmission and storage system and method

[0002] This application claims priority from provisional applications: Serial No. 60/151,846, filed Sep. 1, 1999; and No. 60/167,198, filed Nov. 23, 1999. The following patent applications disclose related subject matter: Ser. No. 09/795,356, filed Nov. 3, 2000; Ser. No. 10/085,548, filed Feb. 27, 2002. These referenced applications have a common assignee with the present application.

[0003] The invention relates to electronic devices, and, more particularly, to speech coding, transmission, storage, and decoding/synthesis methods and circuitry.

[0004] The performance of digital speech systems using low bit rates has become increasingly important with current and foreseeable digital communications. Both dedicated channel and packetized-over-network (e.g., Voice over IP) transmission benefit from compression of speech signals. The widely-used linear prediction (LP) digital speech coding compression method models the vocal tract as a time-varying filter and a time-varying excitation of the filter to mimic human speech. Linear prediction analysis determines LP coefficients a(j), j=1, 2, . . . , M, for an input frame of digital speech samples {s(n)} by setting

[0005] r(n)=s(n)−Σ_M≧j≧1a(j)s(n−j)  (1)

[0006] and minimizing Σr(n)². Typically, M, the order of the linear prediction filter, is taken to be about 10-12; the sampling rate to form the samples s(n) is typically taken to be 8 kHz (the same as the public switched telephone network (PSTN) sampling for digital transmission); and the number of samples {s(n)} in a frame is often 80 or 160 (10 or 20 ms frames). A frame of samples may be generated by various windowing operations applied to the input speech samples. The name “linear prediction” arises from the interpretation of r(n)=s(n)−Σ_M≧j≧1a(j)s(n−j) as the error in predicting s(n) by the linear combination of preceding speech samples Σ_M≧j≧1a(j)s(n−j). Thus minimizing Σr(n)²yields the {a(j)} which furnish the best linear prediction. The coefficients {a(j)} may be converted to line spectral frequencies (LSFs) for quantization and transmission or storage.

[0007] The {r(n)} form the LP residual for the frame and ideally would be the excitation for the synthesis filter 1/A(z) where A(z) is the transfer function of equation (1). Of course, the LP residual is not available at the decoder; so the task of the encoder is to represent the LP residual so that the decoder can generate the LP excitation from the encoded parameters. Physiologically, for voiced frames the excitation roughly has the form of a series of pulses at the pitch frequency, and for unvoiced frames the excitation roughly has the form of white noise.

[0008] The LP compression approach basically only transmits/stores updates for the (quantized) filter coefficients, the (quantized) residual (waveform or parameters such as pitch), and the (quantized) gain. A receiver regenerates the speech with the same perceptual characteristics as the input speech. Periodic updating of the quantized items requires fewer bits than direct representation of the speech signal, so a reasonable LP coder can operate at bits rates as low as 2-3 kb/s (kilobits per second).

[0009] Indeed, the ITU standard G.729 with a bit rate of 8 kb/s uses LP analysis with codebook excitation (CELP) to compress voiceband speech and has performance comparable to that of the 32 kb/s ADPCM in the G.726 standard. In particular, G.729 uses frames of 10 ms length divided into two 5 ms subframes for better tracking of pitch and gain parameters plus reduced codebook search complexity. The second subframe of a frame uses quantized and unquantized LP coefficients while the first subframe interpolates LP coefficients. Each subframe has an excitation represented by an adaptive-codebook part and a fixed-codebook part: the adaptive-codebook part represents the periodicity in the excitation signal using a fractional pitch lag with resolution of 1/3 sample and the fixed-codebook represents the difference between the synthesized residual and the adaptive-codebook representation. 10th order LP analysis with LSF quantization takes 18 bits.

[0010] G.729 handles frame erasures by reconstruction based on previously received information. Namely, replace the missing excitation signal with one of similar characteristics, while gradually decaying its energy by using a voicing classifier based on the long-term prediction gain, which is computed as part of the long-term postfilter analysis. The long-term postfilter sues the long-term filter with a lag that gives a normalized correlation greater than 0.5. For the error concealment process, a 10 ms frame is declared periodic if at least one 5 ms subframe has a long-term prediction gain of more than 3 dB. Otherwise the frame is declared nonperiodic. An erased frame inherits its class from the preceding (reconstructed) speech frame. Note that the voicing classification is continuously updated based on this reconstructed speech signal.

[0011] Leung et al, Voice Frame Reconstruction Methods for CELP Speech Coders in Digital Cellular and Wireless Communications, Proc. Wireless 93 (July 1993) describes missing frame reconstruction using parametric extrapolation and interpolation for a low complexity CELP coder using 4 subframes per frame. In particular, Leung et al proceeds as follows: For frame gain, perform scalar linear extrapolation or interpolation. For LPC coefficients, perform vector linear extrapolation or interpolation (i.e., matrices of extrapolation or interpolation acting of vectors of LPC coefficients to yield reconstructed LPC coefficients). For pitch lag and adaptive codebook coefficients (which are generated for each of the 4 subframes per frame), do median filtering to reconstruct the pitch lag (adjust the pitch search to insure a smooth pitch contour); and adopt a conditional repeat strategy to reconstruct the adaptive codebook coefficients. That is, a voicing decision is made initially for the missing frame by comparing the pitch lag median with the pitch lags in the previous and possibly future frames. If over half of the lags (4 per frame) are within ±5 samples from the median value, the missing frame is declared as voiced. The coefficients can be reconstructed according to one of three methods: (1) if the missing frame is estimated to be unvoiced, then select the scaled version of the coefficients associated with the pitch lag median, (2) if the missing frame is voiced and extrapolation used, then a scaled version of the coefficients of the last subframe of the preceding frame is used, and (3) if the missing frame is voiced and interpolation used, then a scaled version of the coefficient from either the last subframe of the preceding frame or the first subframe of the next frame could be used depending upon whether the pitch median comes from the preceding frame or the next frame. For stochastic excitation gain (generated for each subframe) do vector linear extrapolation or interpolation (i.e., matrices of extrapolation or interpolation acting of vectors of gains to yield reconstructed gains). For stochastic codebook parameters chose random values because of the lesser perceptual importance of these parameters and the fact of the relatively unpredictable behavior of the stochastic excitation.

[0012] However, this extrapolation or interpolation method does not apply to differentially quantized parameters.

[0013] The present invention provides concealment of erased frames which had been differentially quantized by the use of nonlinear interpolation of prior and future received frame information.

[0014] This has advantages including the preferred embodiment use of the time delay and future frame availability of a playout buffer (e.g., as in packetized CELP-encoded voice transmission over a network, including VoIP) for estimating missing parameters for concealment.

[0015] FIG. 1 shows first preferred embodiments.

[0016] FIGS. 2a-2bare schematic diagrams of G.729 encoder and decoder.

[0017] The preferred embodiment methods of concealment of frame erasures in speech transmissions employ both past and future frames and estimate differentially quantized parameters; a nonlinear interpolation. The use of future frames implies time delay, but several systems such as voice over packet networks with playout buffers (used at the receiver to control jitter) already have future frames available and the preferred embodiments take advantage of the existing time delay.

[0018] Preferred embodiment systems and receivers incorporate preferred embodiment methods of error concealment. FIG. 1 illustrates a preferred embodiment receiver for a packet-based system such as VoIP (voice over internet protocol). Packets arriving from the network are first processed by the network module. Statistics are collected, packets ordered and transferred to the playout buffer. If near the time of playout the packet has not yet arrived, it is declared lost and the frame erasure concealment module reconstructs it using both past and future frames. In the figure, missing packet 3 is reconstructed by interpolating the previous packet 2 and following (future) packet 4.

[0019] FIG. 1 shows in functional block format a first preferred embodiment concealment method useful with G.729 encoded speech. G.729 encoding uses 80 bits for every 10 ms frame as follows: line spectrum pairs 18 bits, adaptive codebook index 13 bits split into 8 bits for the first 5 ms subframe and 5 bits for the second subframe, parity 1 bit, fixed codebook index 26 bits split into 13 for each subframe, fixed codebook pulse signs 8 bits split into 4 bits for each subframe, codebook gains 6 bits split as 3 and 3 for stage 1 plus 8 bits split as 4 and 4 for stage 2. FIGS. 2a-2billustrate G.729 encoder and decoder. The first preferred embodiments handle these items as follows.

[0020] LSFs.

[0021] The LSFs for frame m are denoted ω_i[m] for i=1, 2, . . . , 10. The G.729 standard computes estimates {acute over (ω)}_i[m] from the quantized codebook outputs which are differences between LSFs and predicted LSFs based on a moving average of M prior frames. In particular,

[0022] {acute over (ω)}_i[m]=(1−Σ_1≦k≦Mp_i,k)Î_i[m]+Σ_1≦k≦Mp_i,kÎ_i[m−k]  (*)

[0023] where the p_i,kare the coefficients of the moving average predictor and Î_i[m] and Î_i[m−k] for k=1, 2, . . . , M are the codebook outputs for frame m plus M prior frames. (G.729 takes M=4.) There are two predictors (two sets of coefficients) and a bit switches between the two predictors, one strong predictor and one weak predictor, to accommodate change. At the mth frame the vector to be quantized to form Î_i[m] is the normalized difference between the LSF and the predicted LSF:

[0024] Î_i=(ω_i[m]−Σ_1≦k≦Mp_i,kÎ_i[m−k])/(1−Σ_1≦k≦Mp_i,k)

[0025] where the initial conditions are Î_i[j]=iπ/11 for j<0.

[0026] The first preferred embodiments compute the estimates {acute over (ω)}_i[m] for an erased frame m essentially by linear interpolation of the estimates for the preceding frame plus the future frame; namely {acute over (ω)}_i[m]=({acute over (ω)}_i[m+1]+{acute over (ω)}_i[m−1])/2. Of course, {acute over (ω)}_i[m+1] in G.729 depends upon Î_i[m] which was erased, so proceed as follows.

[0027] First, solve equation (*) for Î_i[m]:

[0028] Î_i[m]=({acute over (ω)}_i[m]−Σ_1≦k≦Mp_i,kÎ_i[m−k])/(1−Σ_1≦k≦Mp_i,k)

[0029] Then substitute {acute over (ω)}_i[m]=({acute over (ω)}_i[m+1]+{acute over (ω)}_i[m−1])/2 in to yield:

[0030] Î_i[m]=({acute over (ω)}_i[m+1]/2+{acute over (ω)}_i[m−1]/2−Σ_1≦k≦Mp_i,kÎ_i[m−k])/(1−Σ_1≦k≦Mp_i,k)

[0031] Next, use equation (*) for frame m+1:

[0032] {acute over (ω)}_i[m+1]=(1−Σ_1≦k≦Mp_i,k)Î_i[m+1]+Σ_1≦k≦Mp_i,kÎ_i[m+1−k]

[0033] and substitute the equation for Î_i[m] into the k=1 term of the last sum to give:

[0034] {acute over (ω)}_i[m+1]=(1−

[0035] Σ_1≦k≦Mp_i,k)Î_i[m+1]

[0036] +Σ_2≦k≦Mp_i,kÎ_i[m+1−k]

[0037] +({acute over (ω)}_i[m+1]/2+{acute over (ω)}

[0038] _i[m−1]/2−Σ_1≦k≦Mp_i,kÎ

[0039] _i[m−k])/(1−Σ_1≦k≦Mp_i,k)

[0040] Note that no frame m terms appear in this equation. Simplifying yields:

[0041] {acute over (ω)}_i[m+1]

[0042] =(b_iÎ_i[m+1]+a_i

[0043] {acute over (ω)}_i[m−1]−2a_i

[0044] Σ_1≦k≦Mp_i,kÎ_i

[0045] [m−k]+Σ_2≦k≦Mp_i,kÎ

[0046] _i[m+1−k])/(1−a_i)  (**)

[0047] where a_i=p_i,1/2b_iand b_i=(1−Σ_1≦k≦Mp_i,k).

[0048] Thus the nonlinear interpolation for reconstruction of the erased frame m proceeds through the following steps (1)-(3):

[0049] (1) Compute {acute over (ω)}_i[m+1] using equation (**), this gives the future frame LSFs without using any frame m terms.

[0050] (2) Compute {acute over (ω)}_i[m] using {acute over (ω)}_i[m]=({acute over (ω)}_i[m+1]+{acute over (ω)}_i[m−1])/2 where {acute over (ω)}_i[m+1] comes from step (1) and {acute over (ω)}_i[m−1] is from the preceding frame.

[0051] (3) Compute Î_i[m]=({acute over (ω)}_i[m]−Σ_1≦k≦Mp_i,kÎ_i[m−k])/(1−Σ_1≦k≦Mp_i,k) and use this to update the moving average predictor memory.

[0052] Voicing Classification.

[0053] Advanced error concealment methods for erased speech frames rely on the voicing of the missing frame: different strategies are followed depending on whether the frame is declared voiced or unvoiced. Because the actual voicing of the missing frame is unknown, it is usually assumed that the missing frame has the same voicing as the last correctly received frame. This is clearly non-optimal if the missing frame happens to be at a time of voicing transition between voiced to unvoiced segments or vice versa.

[0054] If future gain and pitch information, as assumed here, is available the voiced/unvoiced classification can be entirely avoided. Gains and pitch, infact, can be interpolated, and the regular procedure of generating an excitation signal composed of a fixed-codebook contribution and an adaptive codebook contribution can be followed.

[0055] Pitch and Gains

[0056] G.729 utilizes an excitation of the LP synthesis filter in each of the two 40-sample subframes per frame; the excitation has the form

[0057] u(n)=ĝ_Pv(n)+ĝ_Cc(n)

[0058] where ĝ_Pis the quantized adaptive-codebook gain g_P, v(n) is the adaptive-codebook vector which is just a pitch delay-interpolation of the prior frame excitation u(n), ĝ_Cis the quantized fixed-codebook gain g_C, and c(n) is the fixed-codebook vector of four pulses (algebraic codebook) with harmonic enhancement. The fixed-codebook gain g_Cis predicted from prior frames analogous to the LSF predictions, so the preferred embodiments generate g_Cfor the subframes of an erased frame in a manner analogous to the preceding for the LSFs.

[0059] In more detail, G.729 proceeds as follows. First, pitch analyses (open-loop and then closed-loop) use correlations of shifts of the (perceptually weighted) speech signal and the reconstructed speech signal to find a delay with fractional sample resolution. The pitch delay is encoded with a total of 14 bits per frame (8 bits plus a parity bit for the first subframe and 5 bits for the second subframe).

[0060] Next, apply the pitch delay to the prior frame excitation u(n) by interpolation to yield an excitation v(n) which LP synthesizes to y(n). The adaptive codebook gain g_P=<x|y>/<y|y> where x(n) is the perceptually-weighted LP synthesized residual.

[0061] Then the difference x(n)−g_Py(n) becomes the target for a search to find a fixed-codebook gain g_Cplus excitation c(n) for minimization of (x(n)−g_Py(n)−g_Cz(n))²where z(n) is perceptually-weighted LP synthesized c(n).

[0062] Analogous to the LSFs, the gain g_Cis predicted from a moving average of prior frame gains and differentially quantized. Indeed, G.729 sets

[0063] g_C=γ{haeck over (g)}_C

[0064] where {haeck over (g)}_Cis a predicted gain based on previous fixed-codebook energies and γ is a correction factor. The mean energy of c(n) is

[0065] E=10 log(Σ_0≦j≦39c(j)²/40)

[0066] Thus the energy of g_Cc(n) is E+20 log(g_C). Then define the mean-removed energy at subframe m by

[0067] E(m)=20 log(g_C(m)).+E−{overscore (E)}

[0068] where {overscore (E)}=30 dB is the mean energy of the fixed-codebook excitation. The gain g_C(m) can be expressed in terms of E(m), E, and {overscore (E)}:

[0069] 20 log(g_C(m))=E(m)+{overscore (E)}−E

[0070] The predicted gain {haeck over (g)}_C(m) is found by predicting the log-energy of the current frame fixed-codebook contribution from the log-energy of previous frame fixed-codebook contribution:

[0071] {haeck over (E)}(m)=Σ_1≦i≦4b_i{haeck over (U)}(m−i)

[0072] where {haeck over (U)}(m) is the quantized version of the prediction error at subframe m, defined by U(m)=E(m)−{haeck over (E)}(m). The predicted gain {haeck over (g)}_C(m) is found through replacement of E(m) by its predicted value in the foregoing equation for g_C(m) in terms of E(m), {haeck over (E)}, and E

[0073] 20 log({haeck over (g)}_C(m))={haeck over (E)}(m)+{haeck over (E)}−E

[0074] The correction factor γ(m) relates to the gain prediction error by U(m)=20 log(γ(m)). The adaptive-codebook gain g_Pand γ are vector quantized using a two-stage conjugate structured codebook; the first stage consists of a 3-bit two-dimensional codebook and the second stage consists of a 4-bit two-dimensional codebook. The first element in each codebook represents the quantized adaptive-codebook gain ĝ_Pand the second element represents the quantized fixed-codebook gain correction factor.

[0075] For the case of frame m missing, but frames m+1 and m−1 plus earlier frames available, the adaptive-codebook gain g_Pcan be interpolated from frames m+1 and m−1 to give a value for frame m, and the fixed-codebook gain correction factor γ can also be interpolated from frames m+1 and m−1 to give a value for frame m. But the predicted fixed-codebook gain {haeck over (g)}_Cfor frame m+1 uses the U(m) from missing frame m. Thus the preferred embodiments proceed analogously to the LSF prediction with missing frames. First, presume a linear interpolation of the fixed-codebook gain:

[0076] g_C(m)=(g_C(m−1)+g_C(m+1))/2

[0077] Thus

[0078] 20 log({haeck over (g)}_C(m+1))=Σ_2≦i≦4b_i{haeck over (U)}(m+1−i)+b₁[20 log((g_C(m−1)+g_C(m+1))/2)−Σ_1≦i≦4b_i{haeck over (U)}(m−i)]+{overscore (E)}−E

[0079] Dividing by 20 b₁and taking exponentials yields

[0080] ({haeck over (g)}_C(m+1))^1/b1=A(g_C(m−1)+g_C(m+1))/2

[0081] where log(A)=(Σ_2≦i≦4b_i{haeck over (U)}(m+1−i)−Σ_1≦i≦4b_i{haeck over (U)}(m−i)]+{overscore (E)}−E)/20b₁So A is positive and known from frame m−1 plus earlier frames. Lastly, substituting {haeck over (g)}_C(m+1)=g_C(m+1)/γ(m+1) gives

[0082] (g_C(m+1))^1/b1(γ(m+1))^−1/b1−A(g_C(m+1)/2=ag_C(m−1))/2

[0083] Note that b₁=0.68, so 1/b₁=1.47. This equation for g_C(m+1) can be solved in terms of items from frame m−1 and earlier frames plus γ(m+1). Then g_C(m) for the missing frame m follows from the original assumption g_C(m)=(g_C(m−1)+g_C(m+1))/2.

[0084] Pitch

[0085] Obtain the pitch for an erased frame by median smoothing of the pitch from the immediately preceding and future frames. More specifically, the first pitch value for the missing frame is obtained by median smoothing of the two pitch values of the last correctly received frame and the first pitch value of the future frame. The second pitch value for the missing frame, instead, is computed as the median of the second pitch value of the last frame and the two pitch values of the future frame.

[0086] The foregoing erased frame concealment for the LSFs can be used without the fixed-codebook gain concealment. Indeed, with past and future frames available, gains and pitch can be interpolated, and the regular procedure of generating an excitation signal composed of a fixed-codebook contribution and an adaptive codebook contribution can be followed.

[0087] Alternatives preferred embodiments change one or both of the presumed linear combinations {acute over (ω)}_i[m]=({acute over (ω)}_i[m+1]+{acute over (ω)}_i[m−1])/2 and g_C(m)=(g_C(m−1)+g_C(m+1))/2 to other functions but otherwise proceed as in the foregoing. With other linear combinations (e.g., coefficients other than 1/2) the computations are similar, but with more involved functions, such as harmonic means, the computations become more involved.

[0088] This section describes in algorithmic form preferred embodiment systems which use the preferred embodiment encoding and decoding in frames with two sub-frames.

[0089] 5.a Pitch

[0090] Step 1. Order (increasing) vector formed by both pitch values of previous frame and first value of future frame;

[0091] Step 2. Select second (median) value as the pitch value to be used in first sub-frame of missing frame;

[0092] Step 3. Order (increasing) vector formed by second value of previous frame and both values of future frame;

[0093] Step 4. Select second (median) value as the pitch value to be used in second sub-frame of missing frame;

[0094] 5.b Adaptive Codebook Gain

[0095] Step 1. Multiply last correctly received adaptive codebook gain by interpolation coefficient a (e.g., 0.75);

[0096] Step 2. Multiply first future adaptive codebook gain by (1−a);

[0097] Step 3. Set first adaptive codebook gain of missing frame to sum of values computed at steps 1 and 2;

[0098] Step 4. Multiply last correctly received adaptive codebook gain by interpolation coefficient b (e.g., 0.25);

[0099] Step 5. Multiply first future adaptive codebook gain by (1−b);

[0100] Step 6. Set second adaptive codebook gain of missing frame to sum of values computed at steps 4 and 5.

[0101] 5.c Line Spectral Frequencies (LSF's)

[0102] Steps to be performed for each LSF (ten in number for G.729).

[0103] Step 1. Sum values of moving average (MA) predictor for future frame and subtract from 1.0;

[0104] Step 2. Multiply value computed at Step 1 by prediction LSF residual for future frame;

[0105] Step 3. Divide the value of the first MA predictor coefficient for future frame by two times value computed at step 1;

[0106] Step 4. Multiply LSF value for past frame by value computed at Step 3;

[0107] Step 5. Compute MA prediction of missing frame (based on LSF residual of last four frames in the case of G.729);

[0108] Step 6. Multiply value computed at Step 5 by two times the value computed at Step 4;

[0109] Step 7. Compute MA prediction of future frame LSF stopping at past frame value (i.e., in the case of G.729, using past frame residual and two residuals prior to that);

[0110] Step 7. Sum the values computed at Steps 2, 4 and 7;

[0111] Step 8. Subtract the value computed at Step 6 from value computed at Step 7;

[0112] Step 9. Divide value computed at Step 8 by value computed at step 3.

[0113] 5.d Fixed Codebook Gain

[0114] Same steps as in 5.c using Fixed-Codebook Gain MA predictor coefficients.

[0115] The preferred embodiments may be modified in various ways while retaining the features of erased frame estimation of parameters encoded as moving averages.

[0116] For example, the interpolation model for the LSF of the erased frame or the fixed-codebook gain could be varied, the moving average predictor coefficients and their number could be varied, and so forth.