Echo Cancellation - A Likelihood Ratio Test for Double-talk Versus Channel Change

Echo cancellers are in wide use in both electrical (four wire to two wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian stochastic input signal model. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, post-detection integration of approximately one hundred sufficient statistic samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01)

A Multidelay Double-Talk Detector Combined with the MDF Adaptive Filter

The multidelay block frequency-domain (MDF) adaptive filter is an excellent candidate for both acoustic and network echo cancellation. There is a need for a very good double-talk detector (DTD) to be combined efficiently with the MDF algorithm. Recently, a DTD based on a normalized cross-correlation vector was proposed and it was shown that this DTD performs much better than the Geigel algorithm and other DTDs based on the cross-correlation coefficient. In this paper, we show how to extend the definition of a normalized cross-correlation vector in the frequency domain for the general case where the block size of the Fourier transform is smaller than the length of the adaptive filter. The resulting DTD has an MDF structure, which makes it easy to implement, and a good fit with an echo canceler based on the MDF algorithm. We also analyze resource requirements (computational complexity and memory requirement) and compare the MDF algorithm with the normalized least mean square algorithm (NLMS) from this point of view.</p

multi-band acoustic echo canceller

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Mingxi Fan.S.B.and M.Eng

Acoustic Echo Cancellation for Human-Robot Communications

This master thesis presents a new efficient method of acoustic echo cancellation targeted at speech recognition for robots. The proposed algorithm features a new double-talk detector, an enhanced initialization and a new noise estimation method. The DTD algorithm is based on the normalized cross-correlation method, uses noise power estimation to be more robust in noisy environment and reacts more accurately to double-talk. The new initialization method switches between two different DTD algorithms to prevent problems during filter convergence. The simple, yet robust Geigel DTD is used during adaptive filter convergence, whereas the program switches to the newly developed DTD after convergence. Finally, the new noise estimation algorithm relies on the output auto-correlation to correctly estimate the noise. To improve speech recognition performance, center clipping is applied on the output of the echo canceler, to further remove the residual echo. White noise is also added to the output signal, in order to make the signal power more stable, which helps the speech recognition engine. Evaluation of the proposed algorithm has been done on a large set of sequences and results have shown that the new algorithm can increase the word recognition rate by up to 80%

System approach to robust acoustic echo cancellation through semi-blind source separation based on independent component analysis

We live in a dynamic world full of noises and interferences. The conventional acoustic echo cancellation (AEC) framework based on the least mean square (LMS) algorithm by itself lacks the ability to handle many secondary signals that interfere with the adaptive filtering process, e.g., local speech and background noise. In this dissertation, we build a foundation for what we refer to as the system approach to signal enhancement as we focus on the AEC problem. We first propose the residual echo enhancement (REE) technique that utilizes the error recovery nonlinearity (ERN) to "enhances" the filter estimation error prior to the filter adaptation. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. SBSS optimized via independent component analysis (ICA) leads to the system combination of the LMS algorithm with the ERN that allows for continuous and stable adaptation even during double talk. Second, we extend the system perspective to the decorrelation problem for AEC, where we show that the REE procedure can be applied effectively in a multi-channel AEC (MCAEC) setting to indirectly assist the recovery of lost AEC performance due to inter-channel correlation, known generally as the "non-uniqueness" problem. We develop a novel, computationally efficient technique of frequency-domain resampling (FDR) that effectively alleviates the non-uniqueness problem directly while introducing minimal distortion to signal quality and statistics. We also apply the system approach to the multi-delay filter (MDF) that suffers from the inter-block correlation problem. Finally, we generalize the MCAEC problem in the SBSS framework and discuss many issues related to the implementation of an SBSS system. We propose a constrained batch-online implementation of SBSS that stabilizes the convergence behavior even in the worst case scenario of a single far-end talker along with the non-uniqueness condition on the far-end mixing system. The proposed techniques are developed from a pragmatic standpoint, motivated by real-world problems in acoustic and audio signal processing. Generalization of the orthogonality principle to the system level of an AEC problem allows us to relate AEC to source separation that seeks to maximize the independence, hence implicitly the orthogonality, not only between the error signal and the far-end signal, but rather, among all signals involved. The system approach, for which the REE paradigm is just one realization, enables the encompassing of many traditional signal enhancement techniques in analytically consistent yet practically effective manner for solving the enhancement problem in a very noisy and disruptive acoustic mixing environment.PhDCommittee Chair: Biing-Hwang Juang; Committee Member: Brani Vidakovic; Committee Member: David V. Anderson; Committee Member: Jeff S. Shamma; Committee Member: Xiaoli M

Implementação eficiente do método da correlação cruzada normalizada para detecção de double-talk

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2009.Este trabalho discute uma implementação eficiente do método da correlação cruzada normalizada (NCC) para detecção de double-talk em sistemas de cancelamento de eco. A principal vantagem desse método é obter um limiar de detecção ótimo independente das características do sistema e dos sinais envolvidos. No entanto, sua implementação direta envolve a inversão de uma matriz . Na prática, utilizam-se versões simplificadas desse método, que têm como desvantagem a dependência dos coeficientes do filtro adaptativo utilizado no cancelamento de eco. A abordagem aqui proposta utiliza uma versão de baixa complexidade computacional do algoritmo recursive least-squares (RLS) para efetuar uma parte importante dos cálculos, eliminando assim a necessidade de algumas aproximações usualmente consideradas. Assim, a implementação proposta depende apenas do conhecimento dos sinais do far-end e do near-end, não levando em conta outras variáveis do sistema tais como os coeficientes do filtro. Uma redução adicional de complexidade é obtida se a detecção de double-talk for realizada em uma freqüência de amostragem menor do que a do sistema. Resultados de simulação mostram muito bom desempenho da abordagem proposta quando comparada com outras técnicas da literatura