Jacob Benesty

Advances in Network and Acoustic Echo Cancellation

This book brings together many advanced topics in network and acoustic echo cancellation aimed towards enhancing the echo cancellation performance of next-generation telecommunication systems. The resulting compendium provides a coherent treatment of such topics not found otherwise in journals or other books.

New insights into the noise reduction Wiener filter

The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion

Springer Handbook of Speech Processing

This article reviews Springer Handbook of Speech Processing by Jacob Benesty, Mohan M. Sondhi, Yiteng Huang , 2008. 1176 pp. Price:

Real-time passive source localization: a practical linear-correction least-squares approach

199.00 (hardcover). ISBN: 978-3-540-49125-5

Adaptive eigenvalue decomposition algorithm for passive acoustic source localization

A linear-correction least-squares estimation procedure is proposed for the source localization problem under an additive measurement error model. The method, which can be easily implemented in a real-time system with moderate computational complexity, yields an efficient source location estimator without assuming a priori knowledge of noise distribution. Alternative existing estimators, including likelihood-based, spherical intersection, spherical interpolation, and quadratic-correction least-squares estimators, are reviewed and comparisons of their complexity, estimation consistency and efficiency against the Cramer-Rao lower bound are made. Numerical studies demonstrate that the proposed estimator performs better under many practical situations.

A better understanding and an improved solution to the specific problems of stereophonic acoustic echo cancellation

To find the position of an acoustic source in a room, the relative delay between two (or more) microphone signals for the direct sound must be determined. The generalized cross-correlation method is the most popular technique to do so and is well explained in a landmark paper by Knapp and Carter. In this paper, a new approach is proposed that is based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the microphone signals contains the impulse responses between the source and the microphone signals (and therefore all the information we need for time delay estimation). In experiments, the proposed algorithm performs well and is very accurate.

Time delay estimation in room acoustic environments: an overview

Teleconferencing systems employ acoustic echo cancelers to reduce echoes that result from coupling between the loudspeaker and microphone. To enhance the sound realism, two-channel audio is necessary. However, in this case (stereophonic sound) the acoustic echo cancellation problem is more difficult to solve because of the necessity to uniquely identify two acoustic paths. We explain these problems in detail and give an interesting solution which is much better than previously known solutions. The basic idea is to introduce a small nonlinearity into each channel that has the effect of reducing the interchannel coherence while not being noticeable for speech due to self masking.

A Nonparametric VSS NLMS Algorithm

Time delay estimation has been a research topic of significant practical importance in many fields (radar, sonar, seismology, geophysics, ultrasonics, hands-free communications, etc.). It is a first stage that feeds into subsequent processing blocks for identifying, localizing, and tracking radiating sources. This area has made remarkable advances in the past few decades, and is continuing to progress, with an aim to create processors that are tolerant to both noise and reverberation. This paper presents a systematic overview of the state-of-the-art of time-delay-estimation algorithms ranging from the simple cross-correlation method to the advanced blind channel identification based techniques. We discuss the pros and cons of each individual algorithm, and outline their inherent relationships. We also provide experimental results to illustrate their performance differences in room acoustic environments where reverberation and noise are commonly encountered.

Acoustic signal processing for telecommunication

The aim of a variable step size normalized least-mean-square (VSS-NLMS) algorithm is to try to solve the conflicting requirement of fast convergence and low misadjustment of the NLMS algorithm. Numerous VSS-NLMS algorithms can be found in the literature with a common point for most of them: they may not work very reliably since they depend on several parameters that are not simple to tune in practice. The objective of this letter is twofold. First, we explain a simple and elegant way to derive VSS-NLMS-type algorithms. Second, a new nonparametric VSS-NLMS is proposed that is easy to control and gives good performances in the context of acoustic echo cancellation

A fast recursive algorithm for optimum sequential signal detection in a BLAST system

List of Figures. List of Tables. Preface. Contributing Authors. 1. An Introduction to Acoustic Echo and Noise Control S.L. Gay, J. Benesty. Part I: Mono-Channel Acoustic Echo Cancellation. 2. The Fast Affine Projection Algorithm S.L. Gay. 3. Subband Acoustic Echo Cancellation Using the FAP-RLS Algorithm: Fixed-Point Implementation Issues M. Ghanassi, B. Champagne. 4. Real-Time Implementation of the Exact Block NLMS Algorithm for Acoustic Echo Control in Hands-Free Telephone Systems B.H. Nitsch. 5. Double-Talk Detection Schemes for Acoustic Echo Cancellation T. Gansler, J. Benesty, S.L. Gay. Part II: Multi-Channel Acoustic Echo Cancellation. 6. Multi-Channel Sound, Acoustic Echo Cancellation, and Multi-Channel Time-Domain Adaptive Filtering J. Benesty, T. Gansler, P. Eneroth. 7. Multi-Channel Frequency-Domain Adaptive Filtering J. Benesty, D.R. Morgan. 8. A Real-time Stereophonic Acoustic Subband Echo Canceler P. Eneroth, S.L. Gay, T. Gansler, J. Benesty. Part III: Noise Reduction Techniques with a Single Microphone. 9. Subband Noise Reduction Methods for Speech Enhancement E.J. Diethorn. Part IV: Microphone Arrays. 10. Superdirectional Microphone Arrays G.W. Elko. 11. Microphone Arrays for Video Camera Steering Yiteng Huang, J. Benesty, G.W. Elko. 12. Nonlinear, Model-Based Microphone Array Speech Enhancement M.S. Brandstein, S.M. Griebel. Part V: Virtual Sound. 13. 3D Audio and Virtual Acoustical Environment Synthesis Jiashu Chen.14. Virtual Sound Using Loudspeakers: Robust Acoustic Crosstalk Cancellation D.B. Ward, G.W. Elko. Part VI: Blind Source Separation. 15. An Introduction to Blind Source Separation of Speech Signals J. Benesty. Index.