Matching pursuits sinusoidal speech coding

Abstract

This paper introduces a sinusoidal modeling technique for low bit rate speech coding wherein the parameters for each sinusoidal component are sequentially extracted by a closed-loop analysis. The sinusoidal modeling of the speech linear prediction (LP) residual is performed within the general framework of matching pursuits with a dictionary of sinusoids. The frequency space of sinusoids is restricted to sets of frequency intervals or bins, which in conjunction with the closed-loop analysis allow us to map the frequencies of the sinusoids into a frequency vector that is efficiently quantized. In voiced frames, two sets of frequency vectors are generated: one of them represents harmonically related and the other one nonharmonically related components of the voiced segment. This approach eliminates the need for voicing dependent cutoff frequency that is difficult to estimate correctly and to quantize at low bit rates. In transition frames, to efficiently extract and quantize the set of frequencies needed for the sinusoidal representation of the LP residual, we introduce frequency bin vector quantization (FBVQ). FBVQ selects a vector of nonuniformly spaced frequencies from a frequency codebook in order to represent the frequency domain information in transition regions. Our use of FBVQ with closed-loop searching contribute to an improvement of speech quality in transition frames. The effectiveness of the coding scheme is enhanced by exploiting the critical band concept of auditory perception in defining the frequency bins. To demonstrate the viability and the advantages of the new models studied, we designed a 4 kbps matching pursuits sinusoidal speech coder. Subjective results indicate that the proposed coder at 4 kbps has quality exceeding the 6.3 kbps G.723.1 coder.