Recognizing Reverberant Speech with RASTA - PLP
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Integrated speech enhancement method using noise suppression and dereverberation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
A binaural room impulse response database for the evaluation of dereverberation algorithms
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
Speech dereverberation based on variance-normalized delayed linear prediction
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
Harmonicity-Based Blind Dereverberation for Single-Channel Speech Signals
IEEE Transactions on Audio, Speech, and Language Processing
Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation
IEEE Transactions on Audio, Speech, and Language Processing
Blind Speech Dereverberation Based on a Statistical Model
ICME '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo
Hi-index | 0.00 |
Model-based late reverberant spectral variance (LRSV) estimator is considered as an effective approach for speech dereverberation, which can construct a simple expression for the LRSV according to the past spectral variance of the reverberant signal. In this paper, we develop a new LRSV estimator based on the time-varying room impulse responses (RIRs) with the assumption that the background noise is comprised of reverberant noise and direct-path noise in a noisy and reverberant environment. In the LRSV estimator, more than one item of past spectral variance of the reverberant signals are used to obtain a smoother shape parameter, which can lead to a better performance for dereverberation compared to the classic methods. Since this shape parameter affected by the estimation error of LRSV may in turn affect the subsequent LRSV estimation, we combine this smoother shape parameter based LRSV estimator with maximum likelihood (ML) algorithm in spectral domain in order to get a more reliable estimation of LRSV. Furthermore, we use the proposed LRSV estimator prior rather than posterior to speech enhancement in noisy and reverberant environment. Experimental results demonstrate our new LRSV estimator is more effective for both noise-free and noisy reverberant speech.