Interpolation of spectral information for low bit rate speech coding

Conventionally, the energy of analysis frames is not taken into account for linear prediction (LPC) interpolation. Incorporating the frame energy improves the subjective quality of interpolation, but increases the spectral distortion (SD). The main reason for this discrepancy is that the outliers are increased in low energy parts of segments with rapid changes in energy. The energy is most naturally combined with a normalized autocorrelation representation.

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Partial-energy weighted interpolation of linear prediction coefficients

Islam

Kabal²

2000 IEEE Workshop on Speech Coding. Proceedings. Meeting the Challenges of the New Millennium (Cat. No.00EX421)

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This paper discusses the interpolation of linear prediction (LP) coefficients. The performance of LP analysis using different numbers of subframes and the choice of representation for the LP coefficients are studied. Interpolation is done by converting the LP coefficients in one of the following representations: line spectral frequencies, reflection coefficients, log area ratios, and autocorrelations. It is shown that good performance is obtained for line spectral frequencies and five subframes per frame. A new interpolation technique which incorporates partial frame energy is introduced. This technique generalizes the concept of energy weighting to different LP coefficient representations.

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Interpolation of spectral information for low bit rate speech coding

Cited by 3 publications

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Spectral stability based event localizing temporal decomposition

Spectral stability based event localizing temporal decomposition

LPC interpolation by approximation of the sample autocorrelation function

Partial-energy weighted interpolation of linear prediction coefficients

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