Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Subspace methods for multimicrophone speech dereverberation
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Applied Signal Processing
Dereverberation by using time-variant nature of speech production system
EURASIP Journal on Advances in Signal Processing
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
EVAM: an eigenvector-based algorithm for multichannel blinddeconvolution of input colored signals
IEEE Transactions on Signal Processing
Precise Dereverberation Using Multichannel Linear Prediction
IEEE Transactions on Audio, Speech, and Language Processing
Codebook-Based Bayesian Speech Enhancement for Nonstationary Environments
IEEE Transactions on Audio, Speech, and Language Processing
Harmonicity-Based Blind Dereverberation for Single-Channel Speech Signals
IEEE Transactions on Audio, Speech, and Language Processing
Dereverberation and Denoising Using Multichannel Linear Prediction
IEEE Transactions on Audio, Speech, and Language Processing
System Identification in the Short-Time Fourier Transform Domain With Crossband Filtering
IEEE Transactions on Audio, Speech, and Language Processing
The multivariate complex normal distribution-a generalization
IEEE Transactions on Information Theory
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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
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
An Improved Method for Late-Reverberant Suppression Based on Statistical Model
Speech Communication
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This paper proposes a method for enhancing speech signals contaminated by room reverberation and additive stationary noise. The following conditions are assumed. 1) Short-time spectral components of speech and noise are statistically independent Gaussian random variables. 2) A room's convolutive system is modeled as an autoregressive system in each frequency band. 3) A short-time power spectral density of speech is modeled as an all-pole spectrum, while that of noise is assumed to be time-invariant and known in advance. Under these conditions, the proposed method estimates the parameters of the convolutive system and those of the all-pole speech model based on the maximum likelihood estimation method. The estimated parameters are then used to calculate the minimum mean square error estimates of the speech spectral components. The proposed method has two significant features. 1) The parameter estimation part performs noise suppression and dereverberation alternately. (2) Noise-free reverberant speech spectrum estimates, which are transferred by the noise suppression process to the dereverberation process, are represented in the form of a probability distribution. This paper reports the experimental results of 1500 trials conducted using 500 different utterances. The reverberation time RT60 was 0.6 s, and the reverberant signal to noise ratio was 20, 15, or 10 dB. The experimental results show the superiority of the proposed method over the sequential performance of the noise suppression and dereverberation processes.