The first book to provide comprehensive and up-to-date coverage of all major speech
enhancement algorithms proposed in the last two decades, Speech Enhancement: Theory …

The traditional minimum mean-square error (MMSE) estimator of the short-time spectral
amplitude is based on the minimization of the Bayesian squared-error cost function. The …

Audio signal enhancement often involves the application of a time-varying filter, or
suppression rule, to the frequency-domain transform of a corrupted signal. Here we address …

Many studies on deep learning-based speech enhancement (SE) utilizing the computational
auditory scene analysis method typically employs the ideal binary mask or the ideal ratio …

Temporal dynamics and speaker characteristics are two important features of speech that
distinguish speech from noise. In this paper, we propose a method to maximally extract …

We propose a new family of Bayesian estimators for speech enhancement where the cost
function includes both a power law and a weighting factor. The parameters of the cost …

This paper presents an algorithm for modulation-domain speech enhancement using a
Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral …

This paper presents a novel probabilistic approach to speech enhancement. Instead of a
deterministic logarithmic relationship, we assume a probabilistic relationship between the …

This paper presents a new approximate Bayesian estimator for enhancing a noisy speech
signal. The speech model is assumed to be a Gaussian mixture model (GMM) in the log …

Speech enhancement (SE) is used in many applications, such as hearing devices, to
improve speech intelligibility and quality. Convolutional neural network-based (CNN-based) …