Auditory sparse representation for robust speaker recognition based on tensor structure
EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications
Reverberant speech enhancement by temporal and spectral processing
IEEE Transactions on Audio, Speech, and Language Processing
Enhanced speech features by single-channel joint compensation of noise and reverberation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Perceptually-motivated selective suppression of late reverberation
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Model-based feature enhancement for reverberant speech recognition
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Model-based dereverberation preserving binaural cues
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Journal of Computer Science and Technology
Design of Sparse Filters for Channel Shortening
Journal of Signal Processing Systems
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Under noise-free conditions, the quality of reverberant speech is dependent on two distinct perceptual components: coloration and long-term reverberation. They correspond to two physical variables: signal-to-reverberant energy ratio (SRR) and reverberation time, respectively. Inspired by this observation, we propose a two-stage reverberant speech enhancement algorithm using one microphone. In the first stage, an inverse filter is estimated to reduce coloration effects or increase SRR. The second stage employs spectral subtraction to minimize the influence of long-term reverberation. The proposed algorithm significantly improves the quality of reverberant speech. A comparison with a recent enhancement algorithm is made on a corpus of speech utterances in a number of reverberant conditions, and the results show that our algorithm performs substantially better.