This correspondence uses a pitch synchronous wavelet transform (PSWT) as an alternative characteristic waveform decomposition method for the waveform interpolation (WI) paradigm. The proposed method has the benefit of providing additional scalability in quantization than the existing WI decomposition to meet desired quality requirements. The PSWT is implemented as a quadrature mirror filter bank and decomposes the characteristic waveform surface into a series of reduced time resolution surfaces. Efficient quantization of these surfaces is achieved by exploiting their perceptual importance and inherent transmission rate requirements. The multiresolution representation has the additional benefit of more flexible parameter quantization, allowing a more accurate description of perceptually important scales, especially at higher coding rates. The proposed PSWT-WI coder is very well suited to high quality speech storage applications.

For efficient coding of speech, it is desirable to separate the slowly and rapidly evolving spectral components to take advantage of their different perceptual qualities. We present a multi-level wavelet decomposition mechanism, using low delay FIR filters, applied to waveform interpolation (WI) coding. The technique overcomes the substantial delay problems encountered by Chong, Burnett, Chicharo and Thomson (see Proc. Int Conf. Acoustics, Speech, Sig. Processing, Seattle, USA, vol. 1., p.513-16, 1998) and identifies a preferred technique for the quantisation of the decomposed surfaces. The phase is shown to be particularly sensitive to the compounding of quantisation errors within the tree-structured transform. The proposed solution involves the use of variable dimension vector quantisation (VDVQ) on separately decomposed magnitude/phase surfaces. This approach provides for coarse or no phase quantisation while maintaining high speech quality. The techniques discussed may also be applied to other transforms and to the quantisation of surfaces in the standard waveform interpolation coder.

A new characteristic waveform decomposition method based on wavelets is proposed for the waveform interpolation (WI) paradigm. In WI, pitch-cycle waveforms are filtered in the evolution domain to decompose the signal into two waveform surfaces, one characterising voiced speech and a second representing unvoiced speech. The slow roll-off of FIR filters leads, however, to a significant inter-relationship between the decomposed surfaces. We present the pitch synchronous wavelet transform (PSWT) as an alternative decomposition mechanism. Filtering is again performed in the evolutionary waveform domain, producing characteristic surfaces at several resolutions. This multi-scale characterisation leads to more flexible quantisation of parameters, especially at higher rates than WI's 2.4 kb/s. FIR filters are replaced in the wavelet filter bank by causal, stable FIR filters which achieve significant delay reductions over their IIR counterparts. Furthermore, FIR filters track the dynamic aspects of the evolutionary surfaces faster, overcoming problems existing in the current WI decomposition.

In this paper, we present an alternative characteristic waveform (CW) decomposition mechanism for the Waveform Interpolation (WI) paradigm based on the Pitch Synchronous Wavelet Transform (PSWT). In this technique, IIR filters replace the conventional FIR filters of the PSWT, offering computational and spectral magnitude performance advantages, in addition to significant delay reductions. Previously, the PSWT has only incorporated filter banks with slowly reacting FIR wavelet filters. While these filters possess the desirable properties of linear phase, and design simplicity, a large delay is incurred which increases exponentially with increasing resolution. The progression to IIR filter banks gives rise to a multi-resolution decomposition mechanism, beneficial for real-time applications, such as speech coding, where delay is an important issue.