Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Step-size control for acoustic echo cancellation filter—an overview
Signal Processing - Special issue on current topics in adaptive filtering for hands-free acoustic communication and beyond
Acoustic Echo and Noise Control: A Practical Approach
Acoustic Echo and Noise Control: A Practical Approach
On the problem of detection and discrimination of double talk and change in the echo path
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Advances in Network and Acoustic Echo Cancellation
Advances in Network and Acoustic Echo Cancellation
Echo Cancellation—A Likelihood Ratio Test for Double-Talk Versus Channel Change
IEEE Transactions on Signal Processing
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
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Echo cancellers (EC) are required in both electrical (impedance 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 optimum likelihood ratio test (LRT) for this problem was studied in a recent paper. The LRT requires a priori knowledge of the background noise and double-talk power levels. Instead, this paper derives a generalized log likelihood ratio test (GLRT) that does not require this knowledge. 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, detection based on about 200 successive samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01) for the theoretical GLRT model. Application of a GLRT-based EC to real voice data shows comparable performance to that of the LRT-based EC given in a recent paper.