Presentation on theme: "Stein VoiceDSP 3.1 Voice DSP Processing III Yaakov J. Stein Chief Scientist RAD Data Communications."— Presentation transcript:
Stein VoiceDSP 3.1 Voice DSP Processing III Yaakov J. Stein Chief Scientist RAD Data Communications
Stein VoiceDSP 3.2 Voice DSP Part 1 Speech biology and what we can learn from it Part 2 Speech DSP (AGC, VAD, features, echo cancellation) Part 3 Speech compression techiques Part 4 Speech Recognition
Stein VoiceDSP 3.4 Encoder Criteria Encoders can be compared in many ways the most important are: Bit rate (Kbps) Speech quality (MOS) Delay (algorithmic [frame+lookahead] + computational + propagation) Computational Complexity Often less important: Bit exactness (interoperability) Transcoding robustness Behavior on non-speech (babble noise, tones, music) Bit error robustness
Stein VoiceDSP 3.7 Military / Satellite Standards
Stein VoiceDSP 3.8 Voice DSP Simple coders
Stein VoiceDSP 3.9 G.711 16 bit linear sampling at 8 KHz means 128 Kbps Minimal toll quality linear sampling is 12 bit (96 Kbps) 8 bit linear sampling (256 levels) is noticeably noisy Due to –prevalence of low amplitudes –logarithmic response of ear we can use logarithmic sampling Different standards for different places
Stein VoiceDSP 3.10 G.711 - cont. -law A-law Although very different looking they are nearly identical G.711 approximates these expressions by 16 staircase straight-line segments (8 negative and 8 positive) -law: horizontal segment through origin, A-law: vertical segment North America = 255 Rest Of World = 87.56
Stein VoiceDSP 3.11 DPCM Due to low-pass character of speech differences are usually smaller than signal values and hence require fewer bits to quantize Simplest Delta-PCM (DPCM) : quantize first difference signal Delta-PCM : quantize difference between signal and prediction s n = p ( s n-1, s n-2, …, s n-N ) = p i s n-i If predict using linear combination (FIR filter), this is linear prediction Delta-modulation (DM) : use only sign of difference (1bit DPCM) Sigma-delta (1bit) : oversample, DM, trade-off rate for bits i
Stein VoiceDSP 3.12 DPCM with prediction If the linear prediction works well, then the prediction error n = s n - s n will be lower in energy and whiter than s n itself ! Only the error is needed for reconstruction, since the predictable portion can be predicted s n = s n + n ! snsn prediction filter snsn nn - prediction filter snsn snsn
Stein VoiceDSP 3.13 DPCM - post-filtering Simplest case : if highly oversampled then previous sample s n-1 predicts s n well, so we can use DM, if sgn( n ) < 0 then - else + For DM there is no way to encode zero prediction error so decoded signal oscillates wildly Standard remedy is a post-filter that low-pass filters this noise But there is a b i g g e r problem!
Stein VoiceDSP 3.14 Open-loop Prediction The encoder (linear predictor) is present in the decoder but there runs as feedback The decoder’s predictions are accurate with the precise error n but it gets the quantized error n and the models diverge! snsn - Q PF nn snsn IQ
Stein VoiceDSP 3.15 Side Information There are two ways to solve the problem... The first way is to send the prediction coefficients from the encoder to the decoder and not to let the decoder derive them The coefficients sent are called side-information Using side-information means higher bit-rate (since both n and coefficients must be sent) The second way does not require increasing bit rate
Stein VoiceDSP 3.16 Closed-loop Prediction To ensure that the encoder and decoder stay “in-sync” we put the decoder into the encoder Thus the encoder’s predictions are identical to the decoder’s and no model difference accumulates snsn snsn IQ PF - Q nn IQ nn
Stein VoiceDSP 3.17 Two types of error For DM there are two types of error (depending on step size) too small too large
Stein VoiceDSP 3.18 Adaptive Step Size Speech signals are very nonstationary We need to adapt the step size to match signal behavior –Increase when signal changes rapidly –Decrease when signal is relatively constant Simplest method (for DM only): –If present bit is the same as previous multiply by K (K=1.5) –If present bit is different, divide by K –Constrain to a predefined range More general method : –Collect N samples in buffer (N = 128 … 512) –Compute standard deviation in buffer –Set to a fraction of standard deviation Send to decoder as side-information or Use backward adaptation (closed-loop computation)
Stein VoiceDSP 3.19 ADPCM G.726 has –Adaptive predictor –Adaptive quantizer and inverse quantizer –Adaptation speed control –Tone and transition detector –Mechanism to prevent loss from tandeming Computational complexity relatively high (10 MIPS) 24 and 16 Kbps modes defined, but not toll quality G.727 same rates but embedded for packetize networks ADPCM only used general low-pass characteristic of speech What is the next step?
Stein VoiceDSP 3.20 Scalar Quantization Standard A/D has preset, evenly distributed levels G.711 has preset, non-evenly distributed levels With a criterion we can make an adaptive quantizer Simplest criterion: minimum squared quantization error n = s n - s n E = n 2 Need algorithm to find optimal placement of levels [EM-type algorithms]
Stein VoiceDSP 3.21 Vector Quantization We can do the same thing in higher dimensions Here we wish to match input data x i i = 1.. N to a codebook of codewords C j j = 1.. M with Minimal Mean Squared Error E = i=1..N | x i - C | 2 where C is the codeword closest to x i in the codebook xixi C1C1 C2C2 C4C4 C3C3
Stein VoiceDSP 3.22 LBG Algorithm for VQ Input x i i = 1.. N[ clustering, unsupervised learning] Randomly initialize codebook C j j = 1.. M Loop until converge: Classification Step for i = 1.. N for j = 1.. M compute D ij 2 = | x i - C j | 2 classify x i to C j with minimal D ij 2 Expectation Step for j = 1.. M correct center C j = i Cj x i 1 NjNj
Stein VoiceDSP 3.23 Speech Application of VQ OK, I understand what to do with scalar quantization what is VQ good for ? We could try to simply VQ frames of speech samples but this doesn’t work well ! We can VQ spectra or sub-band components We often VQ parameter sets (e.g. LPC coefficients) We also VQ model error signals
Stein VoiceDSP 3.24 Voice DSP CELP coders
Stein VoiceDSP 3.25 LPC-10 Based on 10 th order LPC (obviously) [Bishnu Atal] 180 sample blocks are encoded into 54 bits Pitch + U/V (found using AMDF) 7 bits Gain 5 bits 10 reflection coefficients found by covariance method –first two coefficients converted to log area ratios –L 1, L 2, a 3, a 4 5 bits each –a 5, a 6, a 7, a 8 4 bits each –a 9 3 bits a 10 2 bits 41 bits 1 sync bit 1 bit 54 bits 44.44 times per second results in 2400 bps By using VQ could reduce bit rate to under 1 Kbps! LPC-10 speech is intelligible, but synthetic sounding and much of the speaker identity is lost !
Stein VoiceDSP 3.26 The Residual Recover s n by adding back the residual error signal s n = s n + n So if we send n as side-information we can recover s n n is smaller than s n so may require fewer bits ! But n is whiter than s n so may require many bits! The question has now become: How can we compress the residual?
Stein VoiceDSP 3.27 Encoding the Residual RELP (6-9.6 Kbps) Low-pass filter and downsample residual to 1 KHz Encode using ADPCM VQ-RELP (4.8 Kbps) VQ coding of residual RELP (4.8 Kbps) Perform FFT on residual Baseband coding RPE-LTP (GSM-FR at 13 Kbps) Residual Pulse Excitation - Long Term Predictor Perform Long Term Prediction (pitch recovery) Subtract to obtain new residual Decimate by 3, use phase with maximum energy Extract 6-bit overall gain Encode remainder with 3 bits/sample
Stein VoiceDSP 3.28 Residual and Excitation Synthesis filter s n = e n + a m s n-m Analysis filter r n = s n - a m s n-m So r n = e n ! enen all-pole filter snsn snsn rnrn - all-zero filter Note: all-zero filter is the inverse of the all-pole filter excitation residual
Stein VoiceDSP 3.29 CELP Atal’s idea: Find a way to efficiently encode the excitation ! Questions: How can we find the excitation? Theoretically, by algebra (invert the filter!) How can we efficiently encode the residual? VQ - Code Excited Linear Prediction How can we efficiently find the best codeword? Exhaustive search enen LPC snsn
Stein VoiceDSP 3.30 CELP - cont. Atal and friends (Schroeder, Remde, Singhal, etc.) discoveries: Even random codebooks work well [Gaussian, uniform] Don’t need large codebooks [e.g. 1024 codewords for 40 samples] Can center-clip with little loss Codebook with constant amplitude almost as good So we can use codebooks with structure (and save storage/search/bits) Multipulse (MP) Constant Amplitude Pulse Regular Pulse (RP)
Stein VoiceDSP 3.31 Special Excitations Shift technique reduces random CB operations from O(N 2 ) to O(N) [a b c d e f] [c d e f g h] [e f g h I j]... Using a small number of +1 amplitude pulses leads to MIPS reduction Since most values are zero, there are few operations Since amplitudes +1 no true multiplications In a CB containing CW and -CW we can save half Algebraic codebooks exploit algebraic structure Example: choose pulses according to Hadamard matrix Using FHT reduces computation Conjugate structure codebooks Excitation is sum of codewords from two related CBs
Stein VoiceDSP 3.32 Analysis by Synthesis Finding the best codeword by exhaustive search snsn CB...... LPC - find minimum Compute energy
Stein VoiceDSP 3.33 Perceptual Weighting The criterion for selecting the best codeword should be perceptual not simply the energy of the difference signal! We perceptually weight the signal and the synthesized signal PW snsn CBLPCPW - Since PW is a filter we need use it only once snsn CBLPC - PW
Stein VoiceDSP 3.34 Perceptual Weighting - cont. The most important PW effect is masking Coding error energy near formants is not heard anyway so we allow higher error near formants but demand lower perceivable error energy To do this we de-emphasize according to the LPC spectrum! Simplest filter is 1 - a i z -I where a i are the LPC coefficients How do we take the critical bandwidth into account? We perform bandwidth expansion Denominator expansion > numerator 1 - i a i z -I 1 - i a i z -I 1 Typical values: BW = ln( ) F s
Stein VoiceDSP 3.35 Post-filter Not related to the subject, but if we are already here … In order to increase the subjective quality of many coders post-filters are often used to emphasize the formant structure These have the same form as the perceptual weighting filter –but 1 with typical values Denominator expansion < numerator! –the post-filter also reinforces tilt which should then be compensated by an IIR filter –since the spectral valleys are de-emphasized we should change the PW filter parameters and Originally proposed for ADPCM !
Stein VoiceDSP 3.36 Subframes Coders with large frames (> 10 ms) need a long excitation signal and hence a lot of bits to encode An alternative is to divide the frame into (2-4) subframes each of which has its own codeword excitation frame n-1frame n+1frame n ------- LPC ------- CW subframe 1 subframe 2 subframe 3 subframe 4 We really should recompute LPC per subframe but we can get away with interpolating !
Stein VoiceDSP 3.37 Lookahead If we are already dividing up the frame we can compute the LPC based on a shifted frame This is called lookahead, and it adds processing delay ! To decrease delay we can use backward looking IIR filter and then we needn’t send/store the LPC coefficients at all! ------- LPC ------- CW ------- LPC ------- CW
Stein VoiceDSP 3.38 What happened to the pitch? Unlike LPC, the ABS CELP coder is excited by codebook Where does the pitch come from? Random CB: minimi zation will prefer “good” excitation Regular/Multi pulse: pulse spacing (not enough pulses for high pitch) But this is usually not enough (residual has pitch periodicity) Two solutions: Adaptive codebook (Klejn, etal) Long term prediction (Atal + Singhal) Both of these reinforce the pitch component
Stein VoiceDSP 3.39 Adaptive CB Adaptive codebook is repetitions of previous excitations Total excitation is weighted sum of stochastic CB (random, MP, RP, etc) and adaptive CB Adaptive CB Fixed CB LPC GaGa GsGs
Stein VoiceDSP 3.40 Long Term Prediction error computation perceptual weighting Using long-term (pitch predictor) and short-term (LPC) prediction Long term predictor may have only one delay, but then non-integer 1 1 - z - - codebook pitch predictor LPC gain snsn
Stein VoiceDSP 3.41 Federal Standard CELP FS 1016 at 4.8 Kbps has MOS 3.2 Developed by AT&T Bell Labs for DOD 144 bits / 30 ms frame 10 th order LPC on 30 ms Hamming window no pre-emphasis, additional 15 Hz BW expansion (quality and LSP robustness) Conversion to LSP and nonuniform scalar quantization to 34 bits 4 subframes (7.5 ms) LSP interpolation 512 entry fixed CB - static -1,0,+1 from center-clipped Gaussian + 5 bit nonuniform quantized gain 56 bits 256 entry adaptive CB - 8 bits + 5 bit nonuniform quantized gain 48 bits optional noninteger delays, optional Perceptual weighting Postfilter + spectral tilt compensation, removable for noise or tandeming FEC 4 bits SYNC 1 bit reserved 1 bit
Stein VoiceDSP 3.42 G.728 16 Kbps with MOS similar to G.726 at 32 Kbps Low 5 sample (0.625 sec) delay High computational complexity (about 30 MIPS) CELP with Backward LPC LPC order 50 (why not? - we don’t transmit side-information!) Frame of 2.5 ms (20 samples) 4 subframes of 0.625 ms (5 samples) Perceptual weighting Only 10 bit index to fixed CB is transmitted 10 bits per 0.625 ms is 16 Kbps !
Stein VoiceDSP 3.43 G.729 8 Kbps toll-quality coder for DSVD and VoIP Computational complexity 20 MIPS, but G.729a is about 10 MIPS frame 10 ms (80 samples) lookahead 5 ms (1 subframe) LPC, LSP, VQ, LSP interpolation CS-ACELP CB (Interleaved single pulse permutation) 4 [+1] pulses / subframe closed loop pitch prediction and adaptive CB (delay+gain) 2 (40 sample) subframes per frame For each frame the encoder outputs 80 bits LSF coefficients 18 bits pitch 8 bits gain CB 14 bits adaptive CB 5 bits parity check 1 bit pulse positions 26 bits pulse signs 8 bits
Stein VoiceDSP 3.44 G.729 annexes A Compatible reduced complexity encoder with minimal MOS reduction B VAD and CNG C Floating point implementation D 6.4 Kbps version similar to G.729 but 64 output bits per frame, quality better than G.726 at 24Kbps LSF coefficients 18b pitch+adaptive CB 8+4b gain CB 12b fixed CB 22b E 11.8 Kbps coder for high quality and music
Stein VoiceDSP 3.45 G.723.1 6.4 (MP-MLQ) and 5.4 (ACELP) Kbps rates About 18 MIPS on DSP frame 30 ms (240 samples) lookahead 15 ms. LPC on 30 ms (240 sample) frames, LSP and VQ open-loop pitch computation on half-frames (120 sample) excitation on 4 subframes (60 samples) per frame perceptual weighting and harmonic noise weighting fifth-order closed loop pitch predictor MP-MLQ: 5 or 6 [+1] pulses / subframe, positions all even or all odd ACELP: 4 [+1] pulses / subframe, positions differ by 8 Annex A VAD-CNG Annex B floating point implementation
Stein VoiceDSP 3.46 Voice DSP Other Methods MBE/MELP STC/WI
Stein VoiceDSP 3.47 MBE coder LPC10 makes hard U/V decision - no mixed voicing Multi Band Excitation uses a different excitation harmonics of pitch frequency frequency-dependent binary U/V decision large number of sub-bands (>16) Simultaneous ABS estimation of pitch and spectral envelope Then U/V decision made based on spectral fit Use of dynamic programming for pitch tracking V f
Stein VoiceDSP 3.48 MBE coder - cont. DVSI made various MBE, AMBE and IMBE for satellite (INMARSAT) Bit rates 2.4 - 9.6 Kbps (toll quality at 3.6 Kbps) Integral FEC for bit-error robustness As an example: 128 bits for each 20 ms frame pitch 8 bits U/V decisions K bits (K < 12) spectral amplitudes (DCT) 75-K bits FEC (Golay codes) 45 bits
Stein VoiceDSP 3.49 MELP DOD wanted a new 2.4 Kbps coder with MOS similar to FS1016 Main problems with LPC10: –voicing determination errors –no handling of partially voiced speech Unlike MBE MELP uses standard LPC model MELP excitation is pulse train plus random noise Soft decision in small number (5) of sub-bands Frame 22.5 ms (180 samples) 10 th order LPC, 15 Hz BW expansion, LSF, interpolation, VQ pitch refinement 5 sub-bands (0-500-1000-2000-3000-4000Hz) pitch and noise excitation FEC
Stein VoiceDSP 3.50 Sinusoidal Transform Coder McAulay and Quatieri model: instead of LPC use sum of sine waves s n = i = 1.. N A i cos ( i n + i ) For each analysis frame (10 - 20 ms) need to extract N A i & i s Voiced speech Use pitch and important harmonics [from pitch-synchronized STFT] Unvoiced speech Use peaks of STFT [points where slope changes from + to - ] At high bit-rates keep magnitudes, frequencies and phases At low bit-rates frequencies constrained and phases modeled
Stein VoiceDSP 3.51 STC - cont. snsn snsn overlapped windowing FFT peak picker spectrum encoder sum of sinusoids spectrum decoder Sparse spectrum is updated at regularly spaced times Amplitude linearly interpolated between updates Interpolated phase must obey 4 conditions ( ) e.g. all-pole model
Stein VoiceDSP 3.52 STC - cont. Tracking the sinusoidal components birth death frequency time
Stein VoiceDSP 3.53 Waveform Interpolation Voiced speech is a sequence of pitch-cycle waveforms The characteristic waveform usually changes slowly with time Useful to think of waveform in 2d This waveform can be the speech signal or the LPC residual Phase in pitch period time
Stein VoiceDSP 3.54 WI - cont. snsn snsn LPC + pitch tracking Characteristic waveform extraction 2d CW alignment quantization waveform interpolation decoding Per frame LPC and pitch are extracted Represent CW by features (e.g. DFT coefficients) Alignment by circular shift until maximum correlation Separate treatment for voice and unvoiced segments conversion to 1d