Predictive speech signal coding with reduced noise effects

1983 ◽  
Vol 73 (2) ◽  
pp. 717-718
Author(s):  
Bishnu S. Atal ◽  
Manfred R. Schroeder
Keyword(s):  
Speech Signal ◽  
Noise Effects ◽  
Signal Coding

Speech signal coding using correlation values between subframes

1996 ◽  
Vol 99 (1) ◽  
pp. 19
Author(s):  
Keiichi Funaki ◽  
Kazunori Ozawa
Keyword(s):  
Speech Signal ◽  
Signal Coding

A New Approach To Speech Coding: the Neural Predictive Coding

2000 ◽  
Vol 4 (1) ◽  
pp. 120-127 ◽  
Author(s):  
Bruno Gas ◽  
◽  
Jean Luc Zarader ◽  
Cyril Chavy
Keyword(s):  
Discriminant Analysis ◽  
Speech Processing ◽  
Speech Signal ◽  
Predictive Coding ◽  
Class Membership ◽  
New Approach ◽  
Coding Systems ◽  
Signal Coding ◽  
Membership Information ◽  
Connectionist Methods

In this article we propose a new speech signal coding model applied to the recognition of phonemes. This model is an extension to the non linear area of adaptive coding systems used in speech processing. For this purpose, we use predictive connectionist methods. We show that it is possible to take into account class membership information of the phonemes from the stage of coding. To evaluate the NPC encoder, a study of a database of phonemes by discriminant analysis and an application to phonemes recognition are carried out. Simulations presented here show that classification has obviously been improved, compared to currently used types of coding.

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.

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