The Experts below are selected from a list of 30612 Experts worldwide ranked by ideXlab platform
Fu-rong Jean - One of the best experts on this subject based on the ideXlab platform.
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Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
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ICaSSP - Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
Yu-cheng Su - One of the best experts on this subject based on the ideXlab platform.
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Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
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ICaSSP - Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
Guyeon Wei - One of the best experts on this subject based on the ideXlab platform.
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a High-Throughput Maximum a Posteriori Probability Detector
IEEE Journal of Solid-State Circuits, 2008Co-Authors: R Ratnayake, A Kavcic, Guyeon WeiAbstract:This paper presents a Maximum a Posteriori probability (MaP) detector, based on a forward-only algorithm that can achieve high throughputs. The MaP algorithm is optimal in terms of bit error rate (BER) performance and, with Turbo processing, can approach performance close to the channel capacity limit. The implementation benefits from optimizations performed at both algorithm and circuit level. The proposed detector utilizes a deep-pipelined architecture implemented in skew-tolerant domino and experimentally measured results verify the detector can achieve throughputs greater than 750 Mb/s while consuming 2.4 W. The 16-state EEPR4 channel detector is implemented in a 0.13 mum CMOS technology and has a core area of 7.1 mm2.
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a high throughput Maximum a Posteriori probability detector
Custom Integrated Circuits Conference, 2007Co-Authors: R Ratnayake, A Kavcic, Guyeon WeiAbstract:This paper presents a Maximum a Posteriori probability (MaP) detector, based on a forward-only algorithm that can achieve high throughputs. The MaP algorithm is optimal in terms of bit error rate (BER) performance and, with Turbo decoding, can approach performance close to the channel capacity limit. The proposed detector utilizes a deep-pipelined architecture implemented in skew-tolerant domino and experimentally measured results verify the detector can achieve throughputs greater than 750 MHz while consuming 2.4 W. The detector is implemented in a 0.13mum CMOS technology and has a die area of 9.9 mm2.
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CICC - a High-Throughput Maximum a Posteriori Probability Detector
2007 IEEE Custom Integrated Circuits Conference, 2007Co-Authors: R Ratnayake, A Kavcic, Guyeon WeiAbstract:This paper presents a Maximum a Posteriori probability (MaP) detector, based on a forward-only algorithm that can achieve high throughputs. The MaP algorithm is optimal in terms of bit error rate (BER) performance and, with Turbo decoding, can approach performance close to the channel capacity limit. The proposed detector utilizes a deep-pipelined architecture implemented in skew-tolerant domino and experimentally measured results verify the detector can achieve throughputs greater than 750 MHz while consuming 2.4 W. The detector is implemented in a 0.13mum CMOS technology and has a die area of 9.9 mm2.
Yu Tsao - One of the best experts on this subject based on the ideXlab platform.
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Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
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ICaSSP - Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
Jung-en Wu - One of the best experts on this subject based on the ideXlab platform.
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Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.
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ICaSSP - Speech enhancement using generalized Maximum a Posteriori spectral amplitude estimator
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Yu-cheng Su, Jung-en Wu, Yu Tsao, Fu-rong JeanAbstract:This paper proposes a generalized Maximum a Posteriori spectral amplitude (GMaPa) algorithm to spectral restoration for speech enhancement. The proposed GMaPa algorithm dynamically adjusts the scale of prior information to calculate the gain function for spectral restoration. In higher signal-to-noise ratio (SNR) conditions, GMaPa adopts a smaller scale to prevent overcompensations that may result in speech distortions. On the other hand, in lower SNR conditions, GMaPa uses a larger scale to enable the gain function to more effectively remove noise components from noisy speech. We also develop a mapping function to optimally determine the prior information scale according to the SNR of speech utterances. Two standardized speech databases, aurora-4 and aurora-2, are used to conduct objective and recognition evaluations, respectively, to test the proposed GMaPa algorithm. For comparison, three conventional spectral restoration algorithms are also evaluated; they are minimum mean-square error spectral estimator (MMSE), Maximum likelihood spectral amplitude estimator (MLSa), and Maximum a Posteriori spectral amplitude estimator (MaPa). The experimental results first confirm that GMaPa provides better objective evaluation scores than MMSE, MLSa, and MaPa in lower SNR conditions, with comparable scores to MLSa in higher SNR conditions. Moreover, our recognition results indicate that GMaPa outperforms the three conventional algorithms consistently over different testing conditions.