An Acoustic Human-Machine Front-End for Multimedia Applications

<p/> <p>A concept of robust adaptive beamforming integrating stereophonic acoustic echo cancellation is presented which reconciles the need for low-computational complexity and efficient adaptive filtering with versatility and robustness in real-world scenarios. The synergetic combination of a robust generalized sidelobe canceller and a stereo acoustic echo canceller is designed in the frequency domain based on a general framework for multichannel adaptive filtering in the frequency domain. Theoretical analysis and real-time experiments show the superiority of this concept over comparable time-domain approaches in terms of computational complexity and adaptation behaviour. The real-time implementation confirms that the concept is robust and meets well the practical requirements of real-world scenarios, which makes it a promising candidate for commercial products.</p

Using Artificially Reverberated Training Data in Distant Talking ASR

Abstract. Automatic Speech Recognition (ASR) in reverberant rooms can be improved by choosing training data from the same acoustical environment as the test data. In a real-world application this is often not possible. A solution for this problem is to use speech signals from a closetalking microphone and reverberate them artificially with multiple room impulse responses. This paper shows results on recognizers whose training data differ in size and percentage of reverberated signals in order to find the best combination for data sets with different degrees of reverberation. The average error rate on a close-talking and a distant-talking test set could thus be reduced by 29 % relative.

Multichannel Acoustic Signal Processing for Human/Machine Interfaces - Fundamental Problems and Recent Advances

Multichannel signal processing techniques for reproduction and acquisition of audio and speech signals at the acoustic human/machine interface offer spatial selectivity and diversity as additional degrees of freedom over single-channel schemes

Least-Squares Error Beamforming Using Minimum Statistics and Multichannel Frequency-Domain Adaptive Filtering

In this paper we introduce a novel adaptive beamformer which also copes with incoherent background noise. After derivation of the optimum filter based on a weighted time-domain least-squares error criterion we present an efficient realization by applying a multichannel frequency-domain algorithm exhibiting RLS-like convergence. For the computation of this algorithm a simultaneous estimation of the power spectral density matrices of both, the noise signal and the noisy speech signal is necessary. Hence, we propose to use a novel approach based on minimum statistics to achieve this simultaneous estimation. Furthermore, the necessary estimate of a desired signal is generated by using single-channel spectral subtraction. The musical noise is avoided in our approach due to the inherent temporal and spatial averaging of our proposed beamformer. Experimental results show that the algorithm is well-suited for diffuse noise environments (e.g. car noise). Moreover, subjective listening tests confirm that a high speech quality can be obtained. 1