In real-world situations, speech is masked by both background noise and reverberation,
which negatively affect perceptual quality and intelligibility. In this paper, we address …

In real-world environments, human speech is usually distorted by both reverberation and
background noise, which have negative effects on speech intelligibility and speech quality …

This paper presents a novel machine-hearing system that exploits deep neural networks
(DNNs) and head movements for robust binaural localization of multiple sources in …

Speech signal degradation in real environments mainly results from room reverberation and
concurrent noise. While human listening is robust in complex auditory scenes, current …

The acoustic variability of noisy and reverberant speech mixtures is influenced by multiple
factors, such as the spectro-temporal characteristics of the target speaker and the interfering …

A novel end-to-end binaural sound localisation approach is proposed which estimates the
azimuth of a sound source directly from the waveform. Instead of employing hand-crafted …

In recent years, supervised approaches using deep neural networks (DNNs) have become
the mainstream for speech enhancement. It has been established that DNNs generalize well …

Sound source localization from a binaural input is a challenging problem, particularly when
multiple sources are active simultaneously and reverberation or background noise are …

Despite there being a clear evidence for top-down (eg, attentional) effects in biological
spatial hearing, relatively few machine hearing systems exploit the top-down model-based …

Monaural speech separation is a fundamental problem in speech and signal processing.
This problem can be approached from a supervised learning perspective by predicting an …