| 1. |
- Grancharov, Volodya, et al.
(författare)
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Low-complexity, non-intrusive speech quality assessment
- 2006
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Ingår i: IEEE Transactions on Speech and Audio Processing.. - 1558-7916. ; 14:6, s. 1948-1956
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Tidskriftsartikel (refereegranskat)abstract
- Monitoring of speech quality in emerging heterogeneous networks is of great interest to network operators. The most efficient way to satisfy such a need is through nonintrusive, objective speech quality assessment. In this paper, we describe a low-complexity algorithm for monitoring the speech quality over a network. The features used in the proposed algorithm can be computed from commonly used speech-coding parameters. Reconstruction and perceptual transformation of the signal is not performed. The critical advantage of the approach lies in generating quality assessment ratings without explicit distortion modeling. The results from the performed experiments indicate that the proposed nonintrusive objective quality measure performs better than the ITU-T P.563 standard.
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| 2. |
- Grancharov, Volodya, et al.
(författare)
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Non-Intrusive Speech Quality Assessment with Low Computational Complexity
- 2006
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Ingår i: INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING. - BAIXAS : ISCA-INST SPEECH COMMUNICATION ASSOC. - 9781604234497 ; , s. 189-192
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Konferensbidrag (refereegranskat)abstract
- We describe an algorithm for monitoring subjective speech quality without access to the original signal that has very low computational and memory requirements. The features used in the proposed algorithm can be computed from commonly used speech-coding parameters. Reconstruction and perceptual transformation of the signal are not performed. The algorithm generates quality assessment ratings without explicit distortion modeling. The simulation results indicate that the proposed non-intrusive objective quality measure performs better than the ITU-T P.563 standard despite its very low computational complexity.
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| 3. |
- Zhao, David Yuheng, et al.
(författare)
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HMM-based gain-modeling for enhancement of speech in noise
- 2007
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Ingår i: IEEE transactions on speech and audio processing. - 1063-6676 .- 1558-2353. ; 15:3, s. 882-892
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Tidskriftsartikel (refereegranskat)abstract
- Accurate modeling and estimation of speech and noise gains facilitate good performance of speech. enhancement methods using data-driven prior models. In this paper, we propose a hidden Markov model (HMM)-based speech enhancement method using explicit gain modeling. Through the introduction of stochastic gain variables, energy variation in both speech and noise is explicitly modeled in a unified framework. The speech gain models the energy variations of the speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain helps to improve the tracking of the time-varying energy of nonstationary noise. The expectationmaximization (EM) algorithm is used to perform offline estimation of the time-invariant model parameters. The time-varying model'parameters are estimated online using the recursive EM algorithm. The. proposed gain modeling techniques are applied to a novel Bayesian speech estimator, and the performance of the proposed enhancement method is evaluated through objective and subjective tests. The experimental results confirm the advantage of explicit gain modeling, particularly for nonstationary noise sources.
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| 4. |
- Zhao, David Yuheng, et al.
(författare)
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HMM-based speech enhancement using explicit gain modeling
- 2006
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Ingår i: 2006 IEEE International Conference on Acoustics, Speech and Signal Processing. ; , s. 161-164
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Konferensbidrag (refereegranskat)abstract
- We propose a hidden Markov model (HMM) based speech enhancement method using explicit modeling of speech and noise gains. The gains are considered to be stochastic variables in an HMM framework. The speech gain models the energy variations of speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain helps to improve the tracking of the time-varying energy of non-stationary noise. The time-varying parameters of the gain models are estimated on-line using the recursive expectation maximization (EM) algorithm. The performance of the proposed enhancement system is evaluated through both objective and subjective tests. The experimental results confirm the advantage of explicit gain modeling, particularly for non-stationary noise sources.
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| 5. |
- Zhao, David Yuheng, 1977-
(författare)
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Model Based Speech Enhancement and Coding
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In mobile speech communication, adverse conditions, such as noisy acoustic environments and unreliable network connections, may severely degrade the intelligibility and natural- ness of the received speech quality, and increase the listening effort. This thesis focuses on countermeasures based on statistical signal processing techniques. The main body of the thesis consists of three research articles, targeting two specific problems: speech enhancement for noise reduction and flexible source coder design for unreliable networks.Papers A and B consider speech enhancement for noise reduction. New schemes based on an extension to the auto-regressive (AR) hidden Markov model (HMM) for speech and noise are proposed. Stochastic models for speech and noise gains (excitation variance from an AR model) are integrated into the HMM framework in order to improve the modeling of energy variation. The extended model is referred to as a stochastic-gain hidden Markov model (SG-HMM). The speech gain describes the energy variations of the speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain improves the tracking of the time-varying energy of non-stationary noise, e.g., due to movement of the noise source. In Paper A, it is assumed that prior knowledge on the noise environment is available, so that a pre-trained noise model is used. In Paper B, the noise model is adaptive and the model parameters are estimated on-line from the noisy observations using a recursive estimation algorithm. Based on the speech and noise models, a novel Bayesian estimator of the clean speech is developed in Paper A, and an estimator of the noise power spectral density (PSD) in Paper B. It is demonstrated that the proposed schemes achieve more accurate models of speech and noise than traditional techniques, and as part of a speech enhancement system provide improved speech quality, particularly for non-stationary noise sources.In Paper C, a flexible entropy-constrained vector quantization scheme based on Gaus- sian mixture model (GMM), lattice quantization, and arithmetic coding is proposed. The method allows for changing the average rate in real-time, and facilitates adaptation to the currently available bandwidth of the network. A practical solution to the classical issue of indexing and entropy-coding the quantized code vectors is given. The proposed scheme has a computational complexity that is independent of rate, and quadratic with respect to vector dimension. Hence, the scheme can be applied to the quantization of source vectors in a high dimensional space. The theoretical performance of the scheme is analyzed under a high-rate assumption. It is shown that, at high rate, the scheme approaches the theoretically optimal performance, if the mixture components are located far apart. The practical performance of the scheme is confirmed through simulations on both synthetic and speech-derived source vectors.
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| 6. |
- Zhao, David Yuheng, et al.
(författare)
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On noise gain estimation for HMM-based speech enhancement
- 2005
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Ingår i: 9th European Conference on Speech Communication and Technology. ; , s. 2113-2116
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Konferensbidrag (refereegranskat)abstract
- To address the variation of noise level in non-stationary noise signals, we study the noise gain estimation for speech enhancement using hidden Markov models (HMM). We consider the noise gain as a stochastic process and we approximate the probability density function (PDF) to be log-normal distributed. The PDF parameters are estimated for every signal block using the past noisy signal blocks. The approximated PDF is then used in a Bayesian speech estimator minimizing the Bayes risk for a novel cost function, that allows for an adjustable level of residual noise. As a more computationally efficient alternative, we also derive the maximum likelihood (ML) estimator, assuming the noise gain to be a deterministic parameter. The performance of the proposed gain-adaptive methods are evaluated and compared to two reference methods. The experimental results show significant improvement under noise conditions with time-varying noise energy.
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| 7. |
- Zhao, David Yuheng, et al.
(författare)
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Online noise estimation using stochastic-gain HMM for speech enhancement
- 2008
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Ingår i: IEEE transactions on speech and audio processing. - 1063-6676 .- 1558-2353. ; 16:4, s. 835-846
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Tidskriftsartikel (refereegranskat)abstract
- We propose a noise estimation algorithm for single-channel noise suppression in dynamic noisy environments. A stochastic-gain hidden Markov model (SG-HMM) is used to model the statistics of nonstationary noise with time-varying energy. The noise model is adaptive and the model parameters are estimated online from noisy observations using a recursive estimation algorithm. The parameter estimation is derived for the maximum-likelihood criterion and the algorithm is based on the recursive expectation maximization (EM) framework. The proposed method facilitates continuous adaptation to changes of both noise spectral shapes and noise energy levels, e.g., due to movement of the noise source. Using the estimated noise model, we also develop an estimator of the noise power spectral density (PSD) based on recursive averaging of estimated noise sample spectra. We demonstrate that the proposed scheme achieves more accurate estimates of the noise model and noise PSD, and as part of a speech enhancement system facilitates a lower level of residual noise.
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