Improved change prediction for combined beamforming and echo cancellation with application to a generalized sidelobe canceler

Adaptive beamforming and echo cancellation are often necessary in hands-free situations in order to enhance the communication quality. Unfortunately, the combination of both algorithms leads to problems. Performing echo cancellation before the beamformer (AEC-first) leads to a high complexity. In the other case (BF-first) the echo reduction is drastically decreased due to the changes of the beam-former, which have to be tracked by the echo canceler. Recently, the authors presented the directed change prediction algorithm with directed recovery, which predicts the effective impulse response after the next beamformer change and therefore allows to maintain the low complexity of the BF-first structure and to guarantee a robust echo cancellation. However, the algorithm assumes an only slowly changing acoustical environment which can be problematic in typical time-variant scenarios. In this paper an improved change prediction is presented, which uses adaptive shadow filters to reduce the convergence time of the change prediction. For this enhanced algorithm, it is shown how it can be applied to more advanced beamformer structures like the generalized sidelobe canceler and how the information provided by the improved change prediction can also be used to enhance the performance of the overall interference cancellation

Double-talk robust acoustic echo canceller based on CNN filter

Conventional acoustic echo cancellation works by using an adaptive algorithm to identify the impulse response of the echo path. In this paper, we use the CNN neural network filter to remove the echo signal from the microphone input signal, so that only the speech signal is transmitted to the far-end. Using the neural network filter, weights are well converged by the general speech signal. Especially it shows the ability to perform stable operation without divergence even in the double-talk state, in which both parties speak simultaneously. As a result of simulation, this system showed superior performance and stable operation compared to the echo canceller of the adaptive filter structure

Implementation of Stereophonic Acoustic Echo Canceller on Intel IA-32 Processors with SIMD Capability

金沢大学理工研究域　電子情報学系本論文では、ステレオ音響エコーキャンセラのIntel IA-32プロセッサによる効率的な実現方法を検討する。SIMD実現でしばしば遭遇するデータ配置問題を、データ領域の増大なしで解決している。スカラ演算による実現と比較して4倍以上の高速化を達成している

Implementation of stereophonic acoustic echo canceller on nVIDIA GeForce graphics processing unit

金沢大学理工研究域電子情報学系This paper presents an implementation of a stereophonic acoustic echo canceller on nVIDIA GeForce graphics processor and CUDA software development environment. For ef.ciency, fast shared memory has been used as much as possilbe. A tree adder is introduced to reduce the cost for summing thread outputs up. The performance evaluation results suggest that Even a low-cost GPU\u27s with a small number of shader processor greatly helps the echo cancellation for low-cost PC-based teleconferencing. ©2009 IEEE.

Implementation of stereophonic acoustic echo canceller on nVIDIA GeForce graphics processing unit

金沢大学理工研究域電子情報学系This paper presents an implementation of a stereophonic acoustic echo canceller on nVIDIA GeForce graphics processor and CUDA software development environment. For ef.ciency, fast shared memory has been used as much as possilbe. A tree adder is introduced to reduce the cost for summing thread outputs up. The performance evaluation results suggest that Even a low-cost GPU\u27s with a small number of shader processor greatly helps the echo cancellation for low-cost PC-based teleconferencing. ©2009 IEEE.