Development and Testing of a Formant‐Coding Speech Compression System

1956 ◽  
Vol 28 (6) ◽  
pp. 1099-1106 ◽  
Author(s):  
James L. Flanagan ◽  
Arthur S. House
Keyword(s):  
Speech Compression ◽  
Compression System

Adaptive Speech Compression Based on Discrete Wave Atoms Transform

2016 ◽  
Vol 6 (5) ◽  
pp. 2150
Author(s):  
Bousselmi Souha ◽  
Aloui Nouredine ◽  
Cherif Adnane
Keyword(s):  
Compression Ratio ◽  
Speech Quality ◽  
Huffman Coding ◽  
Current Work ◽  
Speech Compression ◽  
Snr Estimation ◽  
Run Length ◽  
Compression System ◽  
Wave Atoms ◽  
Wavelets Transform

<p>This paper proposes a new adaptive speech compression system based on discrete wave atoms transform. First, the signal is decomposed on wave atoms, then wave atom coefficients are truncated using a new adaptive thresholding which depends on the SNR estimation. The thresholded coefficients are quantized using Max Lloyd scalar quantizer. Besides, they are encoded using zero run length encoding followed by Huffman coding. Numerous simulations are performed to prove the robustness of our approach. The results of current work are compared with wavelet based compression by using objective criteria, namely CR, SNR, PSNR and NRMSE. This study shows that the wave atoms transform is more appropriate than wavelets transform since it offers a higher compression ratio and a better speech quality.</p>

scholarly journals Auditory Testing of a Formant‐Coding Speech Compression System

1956 ◽  
Vol 28 (4) ◽  
pp. 768-768
Author(s):  
James L. Flanagan ◽  
Arthur S. House
Keyword(s):  
Speech Compression ◽  
Compression System

An 800 bps speech compression system based on vector quantization

1980 ◽  
Vol 68 (S1) ◽  
pp. S88-S88 ◽  
Author(s):  
David Y. Wong ◽  
Fred B. Juang ◽  
A. H. Gray
Keyword(s):  
Vector Quantization ◽  
Speech Compression ◽  
Compression System

An OLE-based speech compression system for multimedia applications

1998 ◽  
Vol 22 (1) ◽  
pp. 41-48
Author(s):  
T. Srikanthan ◽  
Goh Wee Leng ◽  
S.K. Amarasinghe
Keyword(s):  
Multimedia Applications ◽  
Speech Compression ◽  
Compression System

scholarly journals A new speech compression method

2004 ◽  
Vol 17 (3) ◽  
pp. 391-404
Author(s):  
Miranda Nafornita ◽  
Alexandru Isar ◽  
Dorina Isar
Keyword(s):  
Speech Processing ◽  
Linear Prediction ◽  
Compression Rate ◽  
Acoustic Model ◽  
Speech Compression ◽  
Compression Method ◽  
Threshold Detector ◽  
Compression System ◽  
Orthogonal Transform ◽  
Very High

In this paper a new speech compression method is presented. The traditional speech compression method is based on linear prediction. The compression method, proposed in this paper, is based on the use of an orthogonal transform, the discrete cosine packets transform. This method is well suited for the speech processing, taking into account the sine model of this kind of signals and because this transform converges asymptotically to the Karhunen-Lofleve transform. After the computation of the discrete cosine packets transform, the coefficients obtained are processed with a threshold detector, who keeps only the coefficients superior to a given threshold. This way the number of non zero coefficients is reduced doing the compression. The next block of the compression system is the quantization system. This is build following the speech psycho-acoustic model. The proposed compression method is transparent, the compression rate obtained is important and the operations number and the memory volume used are not very high.

scholarly journals Adaptive Speech Compression Based on Discrete Wave Atoms Transform

2016 ◽  
Vol 6 (5) ◽  
pp. 2150
Author(s):  
Bousselmi Souha ◽  
Aloui Nouredine ◽  
Cherif Adnane
Keyword(s):  
Compression Ratio ◽  
Speech Quality ◽  
Huffman Coding ◽  
Current Work ◽  
Speech Compression ◽  
Snr Estimation ◽  
Run Length ◽  
Compression System ◽  
Wave Atoms ◽  
Wavelets Transform

<p>This paper proposes a new adaptive speech compression system based on discrete wave atoms transform. First, the signal is decomposed on wave atoms, then wave atom coefficients are truncated using a new adaptive thresholding which depends on the SNR estimation. The thresholded coefficients are quantized using Max Lloyd scalar quantizer. Besides, they are encoded using zero run length encoding followed by Huffman coding. Numerous simulations are performed to prove the robustness of our approach. The results of current work are compared with wavelet based compression by using objective criteria, namely CR, SNR, PSNR and NRMSE. This study shows that the wave atoms transform is more appropriate than wavelets transform since it offers a higher compression ratio and a better speech quality.</p>

Sign in / Sign up

Export Citation Format

Share Document