The Experts below are selected from a list of 9387 Experts worldwide ranked by ideXlab platform
M Deriche - One of the best experts on this subject based on the ideXlab platform.
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a new approach to low bit rate Audio coding using a combined harmonic multiband wavelet representation
Information Sciences Signal Processing and their Applications, 1999Co-Authors: M Deriche, D Ning, S BolandAbstract:In this paper, we present updated results for the combined harmonic-wavelet based Audio coder. This coder first approximates the Audio Frame as a sum of several sinusoids using a harmonic analysis-synthesis scheme. The reconstructed harmonic signal is then subtracted from the original to give a residual, which is analysed using a wavelet filtering scheme. After each step (harmonic analysis and wavelet filtering), the perceptually relevant parameters are quantised and encoded. The total least squares (TLS)-Prony algorithm is used for the harmonic analysis scheme, and cascaded M-band wavelet transforms are used for analysing the residual. At encoding bit rates of 60-70 kbit/s, this Audio coder is capable of delivering better results than the MPEG layer II Audio coder operating at the much higher bit rate of 128 kbit/s.
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new results in low bitrate Audio coding using a combined harmonic wavelet representation
International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: S Boland, M DericheAbstract:In this paper, we propose a new combined harmonic-wavelet representation for Audio where a harmonic analysis-synthesis scheme is used, first, to approximate each Audio Frame as a sum of several sinusoids. Then, the difference between the original signal and the reconstructed harmonic signal is analyzed using a wavelet filtering scheme. After each step (harmonic analysis and wavelet filtering), parameters are quantized and encoded. Compared to previously proposed methods, our Audio coder uses different harmonic analysis-synthesis and wavelet filtering schemes. We use the total least squares (TLS)-prony algorithm for the harmonic analysis-scheme, and an M-band wavelet transform for analyzing the residual. Altogether, our proposed coder is capable of delivering excellent Audio signal quality at encoder bitrates of 60-70 kb/s.
S Boland - One of the best experts on this subject based on the ideXlab platform.
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a new approach to low bit rate Audio coding using a combined harmonic multiband wavelet representation
Information Sciences Signal Processing and their Applications, 1999Co-Authors: M Deriche, D Ning, S BolandAbstract:In this paper, we present updated results for the combined harmonic-wavelet based Audio coder. This coder first approximates the Audio Frame as a sum of several sinusoids using a harmonic analysis-synthesis scheme. The reconstructed harmonic signal is then subtracted from the original to give a residual, which is analysed using a wavelet filtering scheme. After each step (harmonic analysis and wavelet filtering), the perceptually relevant parameters are quantised and encoded. The total least squares (TLS)-Prony algorithm is used for the harmonic analysis scheme, and cascaded M-band wavelet transforms are used for analysing the residual. At encoding bit rates of 60-70 kbit/s, this Audio coder is capable of delivering better results than the MPEG layer II Audio coder operating at the much higher bit rate of 128 kbit/s.
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new results in low bitrate Audio coding using a combined harmonic wavelet representation
International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: S Boland, M DericheAbstract:In this paper, we propose a new combined harmonic-wavelet representation for Audio where a harmonic analysis-synthesis scheme is used, first, to approximate each Audio Frame as a sum of several sinusoids. Then, the difference between the original signal and the reconstructed harmonic signal is analyzed using a wavelet filtering scheme. After each step (harmonic analysis and wavelet filtering), parameters are quantized and encoded. Compared to previously proposed methods, our Audio coder uses different harmonic analysis-synthesis and wavelet filtering schemes. We use the total least squares (TLS)-prony algorithm for the harmonic analysis-scheme, and an M-band wavelet transform for analyzing the residual. Altogether, our proposed coder is capable of delivering excellent Audio signal quality at encoder bitrates of 60-70 kb/s.
Weixing Wang - One of the best experts on this subject based on the ideXlab platform.
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an adaptive Audio watermarking method based on local Audio feature and support vector regression
International Conference on Software Engineering, 2009Co-Authors: Hong Peng, Jing Wang, Weixing WangAbstract:Based on local Audio feature and support vector regression (SVR), an adaptive blind Audio watermarking algorithm in wavelet domain is proposed in this paper. The Audio signal is partitioned into Audio Frames, and the watermark is embedded in wavelet domain. For each Audio Frame, the energy and the maximal peaks of its all sub-bands are extracted as the local features, and SVR is used to model the relationship between the local features and the embedding strength of the Audio Frame in order to adaptively control the embedding strength of the Audio Frame. Due to the good learning ability of SVR, the watermark can be correctly extracted under several different attacks. The proposed watermarking method doesn't require the use of the original Audio signal. The experimental results show the proposed algorithm is robust to signal processing, such as lossy compression (MP3), filtering, re-sampling and re-quantizing, etc.
Hong Peng - One of the best experts on this subject based on the ideXlab platform.
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an adaptive Audio watermarking method based on local Audio feature and support vector regression
International Conference on Software Engineering, 2009Co-Authors: Hong Peng, Jing Wang, Weixing WangAbstract:Based on local Audio feature and support vector regression (SVR), an adaptive blind Audio watermarking algorithm in wavelet domain is proposed in this paper. The Audio signal is partitioned into Audio Frames, and the watermark is embedded in wavelet domain. For each Audio Frame, the energy and the maximal peaks of its all sub-bands are extracted as the local features, and SVR is used to model the relationship between the local features and the embedding strength of the Audio Frame in order to adaptively control the embedding strength of the Audio Frame. Due to the good learning ability of SVR, the watermark can be correctly extracted under several different attacks. The proposed watermarking method doesn't require the use of the original Audio signal. The experimental results show the proposed algorithm is robust to signal processing, such as lossy compression (MP3), filtering, re-sampling and re-quantizing, etc.
Jing Wang - One of the best experts on this subject based on the ideXlab platform.
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an adaptive Audio watermarking method based on local Audio feature and support vector regression
International Conference on Software Engineering, 2009Co-Authors: Hong Peng, Jing Wang, Weixing WangAbstract:Based on local Audio feature and support vector regression (SVR), an adaptive blind Audio watermarking algorithm in wavelet domain is proposed in this paper. The Audio signal is partitioned into Audio Frames, and the watermark is embedded in wavelet domain. For each Audio Frame, the energy and the maximal peaks of its all sub-bands are extracted as the local features, and SVR is used to model the relationship between the local features and the embedding strength of the Audio Frame in order to adaptively control the embedding strength of the Audio Frame. Due to the good learning ability of SVR, the watermark can be correctly extracted under several different attacks. The proposed watermarking method doesn't require the use of the original Audio signal. The experimental results show the proposed algorithm is robust to signal processing, such as lossy compression (MP3), filtering, re-sampling and re-quantizing, etc.
