Multirate systems and filter banks
Multirate systems and filter banks
Computational auditory scene analysis
Computational auditory scene analysis
Natural gradient works efficiently in learning
Neural Computation
A double-talk detector for acoustic echo cancellation applications
Signal Processing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Subband-Based Blind Separation for Convolutive Mixtures of Speech
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Blind vector deconvolution: convolutive mixture models in short-time fourier transform domain
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
IEEE Transactions on Signal Processing
Frequency-domain blind deconvolution based on mutual information rate
IEEE Transactions on Signal Processing
Multichannel blind deconvolution for source separation in convolutive mixtures of speech
IEEE Transactions on Audio, Speech, and Language Processing
Blind source separation based on a fast-convergence algorithm combining ICA and beamforming
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Robust Speech Dereverberation Using Multichannel Blind Deconvolution With Spectral Subtraction
IEEE Transactions on Audio, Speech, and Language Processing
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This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT20= 670 ms reverberant environment.