Robust low-complexity backward adaptive pitch predictor for low-delay speech coding

Cuperman, V.; Pettigrew, R.

doi:10.1049/ip-i-2.1991.0044

Cited by 9 publications

(8 citation statements)

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“…Cuperman and Pettigrew [24] indicate a pitch prediction gain of 1 to 5 dB, but of around 2 dB on average. Other sources indicate that the prediction gains can be 3-4 dB [25] or up to 5-6 dB [26].…”

Section: Adaptive Codebook: Long-term Predictionmentioning

confidence: 97%

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

Gibson

2019

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We write the mutual information between an input speech utterance and its reconstruction by a code-excited linear prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short-term predictor, the adaptive codebook, and the fixed codebook. We then show that a recently introduced quantity, the log ratio of entropy powers, can be used to estimate these mutual informations in terms of bits/sample. A key result is that for many common distributions and for Gaussian autoregressive processes, the entropy powers in the ratio can be replaced by the corresponding minimum mean squared errors. We provide examples of estimating CELP codec performance using the new results and compare these to the performance of the adaptive multirate (AMR) codec and other CELP codecs. Similar to rate distortion theory, this method only needs the input source model and the appropriate distortion measure.

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Section: Adaptive Codebook: Long-term Predictionmentioning

confidence: 97%

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

Gibson

2019

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“…For the parameters of the MA component, b i , we utilize a very simple polarity driven gradient algorithm taken from ITU-T G.726/727 [24,25]. The calculation of the lag d, uses a combination of a block-based method using only past model values jointly with a recursive, gradient-like update [26]. The coefficients for the long term prediction component, β i , are propagated based on gradient algorithms [26].…”

Section: Learning (Parameter Adaptation)mentioning

confidence: 99%

“…The calculation of the lag d, uses a combination of a block-based method using only past model values jointly with a recursive, gradient-like update [26]. The coefficients for the long term prediction component, β i , are propagated based on gradient algorithms [26]. The details for all of these are given in the following subsections.…”

Section: Learning (Parameter Adaptation)mentioning

confidence: 99%

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A Reinforcement Learning Approach to Speech Coding

Gibson

2022

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Speech coding is an essential technology for digital cellular communications, voice over IP, and video conferencing systems. For more than 25 years, the main approach to speech coding for these applications has been block-based analysis-by-synthesis linear predictive coding. An alternative approach that has been less successful is sample-by-sample tree coding of speech. We reformulate this latter approach as a multistage reinforcement learning problem with L step lookahead that incorporates exploration and exploitation to adapt model parameters and to control the speech analysis/synthesis process on a sample-by-sample basis. The minimization of the spectrally shaped reconstruction error to finite depth manages complexity and serves as an effective stand in for the overall subjective evaluation of reconstructed speech quality and intelligibility. Different control policies that attempt to persistently excite the system states and that encourage exploration are studied and evaluated. The resulting methods produce reconstructed speech quality competitive with the most popular speech codec utilized today. This new reinforcement learning formulation provides new insights and opens up new directions for system design and performance improvement.

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“…There is significant degradation in speech quality even at an error rate as low as 10W4 [13]. It is possible that this sensitivity can be reduced somewhat by employing the robust pitch predictor proposed by Cuperman and Pettigrew [23].…”

Section: P T C Q With Pitch Predictionmentioning

confidence: 99%

Performance of adaptive prediction algorithms for trellis coded quantization of speech

Sriram

Marcellin

1994

IEEE Trans. Commun.

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This paper investigates the performance of different adaptive prediction algorithms for use in a low delay, high-quality speech coder a t 16 kbit/s. The system t o be studied is based on trellis coded quantization (TCQ). In the work presented, we demonstrate that when trellises based on feedback-free convolutional encoding circqits are employed, error propagation in TCQ systems is not a serious problem. Performance comparisons are made using a fixed predictor and four different adaptive predictors. Informal listening tests reveal that the encoded speech is of good communications quality a t a n error rate of 10W3. In an ideal channel, a system employing a n all-pole least squares lattice predictor with pitch prediction produces reconstructed speech that is nearly indistinguishable from the original.

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Robust low-complexity backward adaptive pitch predictor for low-delay speech coding

Cited by 9 publications

References 1 publication

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

A Reinforcement Learning Approach to Speech Coding

Performance of adaptive prediction algorithms for trellis coded quantization of speech

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