Piecewise uniform scalar quantization technique for Laplacian source with application in speech coding

2011 ◽  
Author(s):  
Jelena Nikolic ◽  
Zoran Peric
Keyword(s):  
Speech Coding ◽  
Scalar Quantization

A Low-Complexity 3.6kbps Speech Coding Algorithm Based on Mixed Excitation

2013 ◽  
Vol 401-403 ◽  
pp. 1282-1286
Author(s):  
Qiang Li ◽  
Li Zhen Wang ◽  
Xu Jiu Xia
Keyword(s):  
Computational Complexity ◽  
High Frequency ◽  
Speech Coding ◽  
Low Complexity ◽  
Speech Quality ◽  
Bit Allocation ◽  
Synthetic Speech ◽  
Scalar Quantization ◽  
Decision Error

A low-complexity 3.6kb/s speech coding algorithm based on mixed excitation is presented in this paper. It uses the parameter encoding and mixed excitation technology to ensure the quality of speech. Through adopting the scalar quantization of Line Spectrum Frequency (LSF), the algorithm reduces the storage and computational complexity. Meanwhile, improved frame type with dynamic Unvoiced/Voiced (U/V) thresholds make a reduction of the traditional U/V decision error and the sudden transformation of U/V frame. A modified bit allocation table is introduced and the PESQ-MOS test shows that the synthetic speech quality has been improved and reached the quality of communication, especially for high frequency female speakers with new frame type.

scholarly journals SWITCHED UNIFORM SCALAR QUANTIZATION ADAPTED TO MEAN AND VARIANCE FOR SPEECH CODING

2018 ◽  
Vol 16 (3) ◽  
pp. 263
Author(s):  
Goran Petković ◽  
Zoran Perić ◽  
Vladimir Despotović
Keyword(s):  
Speech Signal ◽  
Speech Coding ◽  
Average Power ◽  
Performance Measure ◽  
Quantization Noise ◽  
Signal Power ◽  
Scalar Quantization ◽  
Mean And Variance ◽  
Improved Performance ◽  
Signal Coding

Average power and variance are widely used in adaptation techniques in signal coding. A speech signal is usually assumed to be zero-mean; thus an average signal power is equal to the signal variance. However, this assumption is valid only for longer signals with a large number of samples. When the signal is divided into frames (especially if the number of samples within the frame is small) the speech signal within the frame may not be zero-mean. Hence, frame-by-frame adaptation to signal mean might be beneficial. A switched uniform scalar quantizer with adaptation to signal mean and variance is proposed in this paper. The analysis is performed for different frame lengths and the results are compared to an adaptive uniform quantizer that uses adaptation only to average signal power, showing an improved performance. Signal to quantization noise ratio (SQNR) is used as a performance measure.

scholarly journals Generative Speech Coding with Predictive Variance Regularization

2021 ◽  
Author(s):  
W. Bastiaan Kleijn ◽  
Andrew Storus ◽  
Michael Chinen ◽  
Tom Denton ◽  
Felicia S. C. Lim ◽  
...  
Keyword(s):  
Speech Coding
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