Emanuël A. P. Habets

Late Reverberant Spectral Variance Estimation Based on a Statistical Model

In speech communication systems the received microphone signals are degraded by room reverberation and ambient noise that decrease the fidelity and intelligibility of the desired speaker. Reverberant speech can be separated into two components, viz. early speech and late reverberant speech. Recently, various algorithms have been developed to suppress late reverberant speech. One of the main challenges is to develop an estimator for the so-called late reverberant spectral variance (LRSV) which is required by most of these algorithms. In this letter a statistical reverberation model is proposed that takes the energy contribution of the direct-path into account. This model is then used to derive a more general LRSV estimator, which in a particular case reduces to an existing LRSV estimator. Experimental results show that the developed estimator is advantageous in case the source-microphone distance is smaller than the critical distance.

Blind estimation of reverberation time based on the distribution of signal decay rates

The reverberation time is one of the most prominent acoustic characteristics of an enclosure. Its value can be used to predict speech intelligibility, and is used by speech enhancement techniques to suppress reverberation. The reverberation time is usually obtained by analysing the decay rate of (i) the energy decay curve that is observed when a noise source is switched off, and (ii) the energy decay curve of the room impulse response. Estimating the reverberation time using only the observed reverberant speech signal, i.e., blind estimation, is required for speech evaluation and enhancement techniques. Recently, (semi) blind methods have been developed. Unfortunately, these methods are not very accurate when the source consists of a human speaker, and unnatural speech pauses are required to detect and/or track the decay. In this paper we extract and analyse the decay rate of the energy envelope blindly from the observed reverberation speech signal in the short-time Fourier transform domain. We develop a method to estimate the reverberation time using a property of the distribution of the decay rates. Experimental results using simulated and real reverberant speech signals demonstrate the performance of the new method.

Joint Dereverberation and Residual Echo Suppression of Speech Signals in Noisy Environments

Hands-free devices are often used in a noisy and reverberant environment. Therefore, the received microphone signal does not only contain the desired near-end speech signal but also interferences such as room reverberation that is caused by the near-end source, background noise and a far-end echo signal that results from the acoustic coupling between the loudspeaker and the microphone. These interferences degrade the fidelity and intelligibility of near-end speech. In the last two decades, post filters have been developed that can be used in conjunction with a single microphone acoustic echo canceller to enhance the near-end speech. In previous works, spectral enhancement techniques have been used to suppress residual echo and background noise for single microphone acoustic echo cancellers. However, dereverberation of the near-end speech was not addressed in this context. Recently, practically feasible spectral enhancement techniques to suppress reverberation have emerged. In this paper, we derive a novel spectral variance estimator for the late reverberation of the near-end speech. Residual echo will be present at the output of the acoustic echo canceller when the acoustic echo path cannot be completely modeled by the adaptive filter. A spectral variance estimator for the so-called late residual echo that results from the deficient length of the adaptive filter is derived. Both estimators are based on a statistical reverberation model. The model parameters depend on the reverberation time of the room, which can be obtained using the estimated acoustic echo path. A novel postfilter is developed which suppresses late reverberation of the near-end speech, residual echo and background noise, and maintains a constant residual background noise level. Experimental results demonstrate the beneficial use of the developed system for reducing reverberation, residual echo, and background noise.

Temporal selective dereverberation of noisy speech using one microphone

Reverberant speech can be described as sounding distant with noticeable coloration and echo. These detrimental perceptual effects are caused by early and late reflections, respectively, and reduces the fidelity and intelligibility of speech. It is well-known that the echo density of the reflections increases with time. Therefore, the temporal structure of early and late reflections differs. In this paper, we combine two different dereverberation techniques that were recently developed to suppress early and late reverberation separately. First, late reverberation is suppressed using a spectral processing technique that is based on a statistical reverberation model. Secondly, early reverberation and residual late reverberation are suppressed using a linear prediction (LP) residual processing technique. In addition, an objective measure based on the kurtosis of the LP residual is proposed to measure the coloration caused by early reflections. Experimental results demonstrate the beneficial use of the new single microphone system that reduces echo and coloration with little speech distortion.

Speech dereverberation using backward estimation of the late reverberant spectral variance

In speech communication systems the received microphone signals are degraded by room reverberation and ambient noise. This signal degradation can decrease the fidelity and intelligibility of the desired speaker. Reverberant speech can be separated into two components, viz. an early speech component and a late reverberant speech component. Reverberation suppression algorithms, that are feasible in practice, have been developed to suppress late reverberant speech or in other words to estimate the early speech component. The main challenge is to develop an estimator for the so-called late reverberant spectral variance (LRSV). In this contribution a generalized statistical reverberation model is proposed that can be used to estimate the LRSV. Novel and existing estimators can be derived from this model. One novel estimator is a so-called backward estimator that uses an estimate of the early speech component to obtain an estimate of the LRSV. Advantages and possible disadvantages of the estimators are discussed, and experimental results using simulated reverberant speech are presented.

Towards multi-microphone speech dereverberation using spectral enhancement and statistical reverberation models

In speech communication systems the received microphone signals are degraded by room reverberation and ambient noise. Reverberant speech can be separated into two components, viz. an early speech component and a late reverberant speech component. In this paper a multichannel dereverberation algorithm is proposed to suppress late reverberation. Specifically, we employ a minimum variance distortionless beamformer and a single-channel MMSE estimator, which operates on the beamformers output signal. The so-called late reverberant spectral variance (LRSV) required by the MMSE estimator can be estimated using i) the beamformers output signal or ii) the received microphone signals. In this contribution we investigate both approaches and show how a priori knowledge of the reverberant sound field can be exploited to improve the LRSV estimation. Advantages and disadvantages of the LRSV estimators are discussed, and experimental results using simulated reverberant speech are presented.

Dual-microphone speech dereverberation using GARCH modeling

In this paper, we develop a dual-microphone speech dereverberation algorithm for noisy environments, which is aimed at suppressing late reverberation and background noise. The spectral variance of the late reverberation is obtained with adaptively-estimated direct path compensation. A Markov-switching generalized autoregressive conditional heteroscedasticity (GARCH) model is used to estimate the spectral variance of the desired signal, which includes the direct sound and early reverberation. Experimental results demonstrate the advantage of the proposed algorithm compared to a decision-directed-based algorithm.

Multimicrophone speech dereverberation using spatiotemporal and spectral processing

Speech signals acquired in a reverberant room with microphones positioned at a distance from the talker are degraded in quality due to reverberation and measurement noise. Therefore, enhancement of reverberant speech is important in hands-free telecommunications applications. The perceptual effects of reverberation can be linked to the room impulse response (RIR) between the talker and the microphone and are characterized by: (i) colouration, due to the strong early reflections and (ii) a distant echoey quality due to the decaying tail of the RIR. Accordingly, we present a two-stage multimicrophone method for speech dereverberation. First, spatiotemporal averaging is performed on the linear prediction residual, which primarily reduces the effects of the early reflections. Secondly, a spectral subtraction method is employed to reduce late reverberation. Simulation results with measured RIRs and additive white Gaussian noise illustrate the performance of this method and show that the combined approach performs better than each of the two stages individually.

On a tradeoff between dereverberation and noise reduction using the MVDR beamformer

The minimum variance distortionless response (MVDR) beamformer can be used for both speech dereverberation and noise reduction. In this paper we analyse the tradeoff between the amount of speech dereverberation and noise reduction achieved by the MVDR beamformer. We show that the amount of noise reduction that is sacrificed when desiring both speech dereverberation and noise reduction depends on the direct-to-reverberation ratio of the acoustic transfer function between the desired source and a reference microphone. The performance evaluation supports the theoretical analysis and demonstrates the tradeoff between speech dereverberation and noise reduction.

Signal-Dependent Array Processing

In this chapter, we derive spherical harmonic domain signal-dependent beamformers, whose weights depend on the second-order statistics of the desired signal and/or of the noise to be suppressed. These beamformers adaptively seek to achieve optimal performance in terms of noise reduction and speech distortion.