Reducing the bit rate of waveform interpolation speech coders while maintaining the perceptual quality has been the focus of a great deal of research. This letter proposes a new method of slowly evolving waveform (SEW) quantization specifically targeted at low rate coding. The proposed method uses a pulse model whose parameters are implicitly contained in the quantized rapidly evolving waveform (REW) parameters, thus requiring no bits for transmission. Results indicate no degradation in perceptual speech quality when compared to that of the existing SEW quantization method. This retention of perceptual quality is in spite of a 12% reduction in the overall coder bit rate

Burnett, I.

Lukasiak, J.

English

Research Online

University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences August 2001 Low rate WI SEW representation using a REW-implicit pulse model J. Lukasiak University of Wollongong, jl01@uow.edu.au I. Burnett University of Wollongong, ianb@uow.edu.au Follow this and additional works at: https://ro.uow.edu.au/infopapers  Part of the Physical Sciences and Mathematics Commons Recommended Citation Lukasiak, J. and Burnett, I.: Low rate WI SEW representation using a REW-implicit pulse model 2001. https://ro.uow.edu.au/infopapers/18 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: research-pubs@uow.edu.au Low rate WI SEW representation using a REW-implicit pulse model Abstract Reducing the bit rate of waveform interpolation speech coders while maintaining the perceptual quality has been the focus of a great deal of research. This letter proposes a new method of slowly evolving waveform (SEW) quantization specifically targeted at low rate coding. The proposed method uses a pulse model whose parameters are implicitly contained in the quantized rapidly evolving waveform (REW) parameters, thus requiring no bits for transmission. Results indicate no degradation in perceptual speech quality when compared to that of the existing SEW quantization method. This retention of perceptual quality is in spite of a 12% reduction in the overall coder bit rate. Keywords low rate speech coding, slowly evolving waveform, waveform interpolation Disciplines Physical Sciences and Mathematics Publication Details This article was originally published as: Lukasiak, J & Burnett, I, Low rate WI SEW representation using a REW-implicit pulse model, IEEE Signal Processing Letters, August 2001, 8(8), 228-230. Copyright IEEE 2001. This journal article is available at Research Online: https://ro.uow.edu.au/infopapers/18 228 IEEE SIGNAL PROCESSING LETTERS, VOL. 8, NO. 8, AUGUST 2001Low Rate WI SEW Representation Using aREW-Implicit Pulse ModelJ. Lukasiak, Student Member, IEEE,and I. S. Burnett, Member, IEEEAbstract—Reducing the bit rate of waveform interpolationspeech coders while maintaining the perceptual quality hasrecently been the focus of a great deal of research. This letterproposes a new method of slowly evolving waveform (SEW)quantization specifically targeted at low rate coding. The pro-posed method uses a pulse model whose parameters are implicitlycontained in the quantized rapidly evolving waveform (REW) pa-rameters, thus requiring no bits for transmission. Results indicateno degradation in perceptual speech quality when compared tothat of the existing SEW quantization method. This retention ofperceptual quality is in spite of a 12% reduction in the overallcoder bit rate.Index Terms—Low-rate speech coding, slowly evolving wave-form (SEW), waveform interpolation (WI).I. INTRODUCTIONT HE WAVEFORM interpolation (WI) paradigm proposedby Kleijn [1] is the focus of much current research inspeech coding circles. The WI paradigm involves first linearpredictive (LP) filtering the input speech. The residual signal isthen separated into pitch cycles (known as characteristic wave-forms (CW) [1]) and these are used to form a two-dimensional(2-D) waveform, which evolves on a pitch synchronous nature.To maximize the smoothness of the 2-D surface the individualpitch length segments are aligned when constructing the surface.This 2-D waveform is then decomposed into Slowly evolvingand Rapidly evolving waveforms (SEW/REW). The SEW andREW are down sampled and quantized separately in the en-coder. The decoder reconstructs the SEW and REW via inter-polation before recombining them. Synthesized speech is pro-duced by converting the reconstructed 2-D surface back to aone-dimensional (1-D) signal and passing this signal throughthe linear predictive synthesis filter.The current research involving WI can be broadly groupedinto distinct categories these being a) low rate speech coding,and b) perfect reconstruction allowing waveform coding.A commonality in much of the present research involvesidentifying better means of representing the SEW to achieveimproved perceptual quality [2], [3]. The original WI coder [1]operating at approximately 2.4 kbps quantizes and transmitsonly the SEW DFT magnitude values. A phase model is usedManuscript received October 11, 2000. This work was suported in part byan Australian Postgraduate Award (Industry) and a Motorola (Australia) Part-nerships in Research grant, and Motorola, Inc., Australia. The associate editorcoordinating the review of this manuscript and approving it for publication wasProf. Y. Shoham.The authors are with Whisper Laboratories, TITR, University of Wollongong,Wollongong, NSW, Australia 2522 (e-mail: jl01@ouw.edu.au).Publisher Item Identifier S 1070-9908(01)06117-X.in the decoder to reconstruct the SEW. New methods proposedfor better representation of the SEW waveform include directlyquantizing the SEW DFT phase in an analysis by synthesisstructure (AbyS) [2] and critically sampling and warping toa constant length to achieve perfect reconstruction [3]. Thesemethods report improved perceptual quality but at the expenseof increased complexity and bit rate.We propose a new method of SEW transmission specificallytargeted at low rate coding. The method proposed uses a pulsemodel whose parameters are implicitly contained in the quan-tized REW parameters, to represent the SEW.This letter is organized as follows. Section II introduces thenew SEW quantization technique and also defines the existingscheme. Section III compares the perceptual performance of thenew technique with the existing SEW quantization method. Fi-nally the major points are summarized in Section IV.II. L OW RATE SEW QUANTIZATIONA. New Quantization MethodA new scheme to quantize and reconstruct the SEW isproposed. This method uses a new pulse modeling mechanismfor reconstruction of the SEW waveform together with phasematching when recombining the REW and SEW waveforms.The use of a pulse model for the SEW results in no bits beingused for transmission of this parameter. The model used is basedon the Zinc function which is defined in the discrete time domainas [4]oddeven.(1)The zinc model has been found to be superior in modeling theLP residual and is widely used in time domain analysis by syn-thesis schemes where the parameters, , and are selectedto minimize the error between the pulse and the residual signal[4]. To allow the zinc pulse to be conveniently used in the WIstructure, the pulse was translated to the frequency domain viathe DFT. This allows straightforward interpolation between ad-jacent pulses of different lengths via zero padding. For low ratecoding, the parameters, and cannot be transmitted due tobit rate constraints. It was found that speech of high perceptualquality could be produced by settingto zero. This forces thezinc pulse to be wholly positive and also places the pulse peakat the beginning of the frame. Positioning the pulse at the begin-ning of each frame is equivalent to removing the SEWs linear1070–9908/01$10.00 © 2001 IEEELUKASIAK AND BURNETT: LOW RATE WI SEW REPRESENTATION 229phase component, which is acceptable in WI as the linear phaseis already modified by aligning the waveforms in the encodingstage prior to down sampling. The values ofand are setaccording toREW (2)Equation (2) uses the assumptions that the LP residual spec-trum is flat and that spectrum has been normalized to unity valuepreviously in the coding process by the removal of the gain term.Using these assumptions and the properties of the inverse dis-crete Fourier transform it can be shown that the resultant valueof (2) is equal to the height of a single sample time domain pulsethat would exhibit the required flat magnitude spectrum. Whilethe zinc pulse consists of a large initial pulse followed by furtherimpulses whose amplitudes decrease rapidly with time, informalperceptual testing has shown that setting the height of the initialpulse equal to the height of a single impulse, as calculated by(2), produces good results.The method of deriving an implicit SEW pulse from the REWallows the pulse height to be dynamically varied according tothe magnitude of the REW. Thus, for sections with a high noisecontent the pulse is small and vice versa. Forcing the pulse tobe positive and of fixed position may appear to be suboptimalin modeling the SEW. However, initial perceptual testing indi-cated a preference for this configuration over a pulse of variableposition and polarity.B. Existing WI SEW Quantization [1]The DFT magnitude coefficients for the lowest 800 Hz arequantized using a seven-bit Vector quantizer. The remainingDFT magnitude coefficients are calculated from the recon-structed REW using the fact that above 800 Hz, the overallresidual signal magnitude spectrum may be considered flat.The SEW phase spectrum is not transmitted but is set equal tothat of a predetermined model. The bit rate required for thismethod is 280 bps for the frame size used.C. Recombination of the SEW and REWIn [1] the synthesized CW is generated by adding the Fourierseries coefficients representing the SEW and REW. However,due to the prior discarding of both the SEW and REW phaseinformation, this method of recombination does not ensure con-structive rather than destructive recombination. Also it is pos-sible that the REW may introduce an extra pulse into the re-constructed section. One option for improving the recombina-tion procedure is to time align the entire SEW and REW wave-forms. This method works well for the SEW quantized usingthe existing scheme. However, for the new zinc pulse schemetime aligning before recombination does not work well and pro-duces hiss in the synthesized speech. This is due to the fact thatthe zinc pulse detailed in Section II-A exhibits a large initialimpulse followed by further impulses of decreasing magnitude.This characteristic makes the maximum correlation criteria fortime alignment unreliable as the correlation due to the subse-quent impulses can tend to subtract from the correlation forTABLE IMOS TEST RESULTSthe initial impulse, thus causing the waveforms to align incor-rectly. To achieve good reconstruction with the zinc pulse SEW,a method that matches the REW phase to that of the SEW below800 Hz was developed. This ensures the phase of the recon-structed section is matched to the SEW phase below 800 Hz andis a combination of the ratio of SEW to REW phase above thisfigure [5]. Matching the low frequency phase where the SEWmagnitude is dominant ensures that these low frequencies arealigned in the time domain and thus produce a degree of tem-poral masking around the SEW pulse. This masking removesthe hiss from the reconstructed signal.To determine the individual effect of the phase matchingmethod, it was tested with the existing SEW quantized wave-forms. Informal listening tests found that when compared totime alignment, the phase matching method resulted in neitheran improvement nor degradation in the perceptual quality ofthe reconstructed speech.III. EXPERIMENTAL RESULTSA. Coder ConfigurationThe structure of the coder is as detailed in [1]. The coderallocates 26 bits for the LSF parameters, ten bits for the power,six bits for the pitch and eight bits for the REW waveform perframe, with a frame size of 25 ms. The eight bits allocated to theREW are used to represent the REW magnitude spectrum withrandom phase used in the decoder to reconstruct the REW. TheREW quantization scheme used is the same as that detailed [1].The overall bit rate of the base coder is 2 kbps. When the SEWis quantized using the existing method, 280 bps must be addedto this value.B. Subjective Test ResultsFor testing purposes, the coder detailed in Section III-A wasused to code eight input speech sentences (four male, four fe-male) from the TIMIT database using both of the SEW repre-sentations. Mean opinion score (MOS) testing was carried outusing 24 untrained listeners each using Sony headphones. Theresults are shown in Table I.The MOS scores indicate that the pulse model SEW represen-tation produced no degradation in the quality of the synthesizedspeech when compared to the existing method. This is despitethe pulse model requiring no bits for transmission compared to280 bps for the existing method.This indicates that for low rate WI coding, attempting tocoarsely quantize and preserve the shape of the SEW withthe limited number of bits available, offers no perceptual230 IEEE SIGNAL PROCESSING LETTERS, VOL. 8, NO. 8, AUGUST 2001improvement when compared to representing the SEW witha fixed shape zinc model that is smoothly evolving and thuseasily interpolated.IV. CONCLUSIONThe bit rate saving and preservation of perceptual quality of-fered by the new pulse model representation of the SEW offers asignificant advantage for low rate WI coding. The result is a 12%reduction in the overall coder bit rate. Alternately, the coder bitrate could be maintained and the extra bits used to more accu-rately quantize other parameters such as the LSF or the REW. Asthe parameters of the pulse model are inferred in the REW wave-form, a more accurate representation of this waveform shouldalso produce an improvement in the SEW quality, thus offeringtwo contributions toward improving the overall quality of thecoder.In conclusion, the results suggest that to achieve the besttradeoff for bit rate and perceptual quality, WI should eitherquantize the SEW very accurately requiring a higher bit rateas in [2] and [3] or opt for a parametric representation that issmoothly evolving and thus easily interpolated. Attempting tocoarsely maintain the SEW shape using a limited bit count ap-pears to offer little benefit.REFERENCES[1] W. B. Kleijn and J. Haagen, “Waveform interpolation for coding andsynthesis,” inSpeech Coding and Synthesis, W. B. Kleijn and K. K.Paliwal, Eds. New York: Elsevier, 1995, pp. 175–207.[2] O. Gottesman, “Dispersion phase vector quantization for enhancementof waveform interpolative coder,” inProc. Int. Conf. Acoustics, Speech,and Signal Processing ’99, Phoenix, AZ, 1999.[3] N. R. Chong, I. S. Burnett, and J. F. Chicharo, “Adapting waveforminterpolation (with pitch spaced subbands) for quantization,” inProc.IEEE Workshop Speech Coding, Poorvoo, Finland, 1999, pp. 96–98.[4] D. J. Hiotakakos and C. S. Xydeas, “Low bit rate coding using an in-terpolated zinc excitation model,” inProc. Int. Conf. Acoustics, Speech,Signal Processing ’94, vol. 3, Adelaide, Australia, 1994, pp. 884–997.[5] H.-G. Kang and D. Sen, “Phase adjustment in waveform interpolation,”in Proc. Int. Conf. Acoustics, Speech, Signal Processing’99, vol. 1,Phoenix, AZ, 1999, pp. 261–264.

Low rate WI SEW representation using a REW-implicit pulse model

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Low rate WI SEW representation using a REW-implicit pulse model

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