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.

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 expectation-maximization (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

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

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 speechcoding 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. Index Terms: non-intrusive quality assessment, quality of service. 1.

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

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.

Multiple-description coding is a joint source- and channel coding technique suitable for real-time multimedia transmission over erasure channels. This work improves the previous methods of multiple-description vector quantization using lattice structured codebooks by introducing translated lattices in the single-description codebooks. The quantizer can easily adapt to the current channel conditions, using the locally optimized combined-description codebooks, assuming that channel statistics are available at the encoder. Compared to previous methods, the central distortion is greatly reduced for noisy channels, without a significant effect on complexity.