Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …

This paper describes several important methods for the blind source separation of audio
signals in an integrated manner. Two historically developed routes are featured. One started …

This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

Models for audio source separation usually operate on the magnitude spectrum, which
ignores phase information and makes separation performance dependant on hyper …

Speech enhancement and separation are core problems in audio signal processing, with
commercial applications in devices as diverse as mobile phones, conference call systems …

Speech enhancement and automatic speech recognition (ASR) are most often evaluated in
matched (or multi-condition) settings where the acoustic conditions of the training data …

We study the use of the Wave-U-Net architecture for speech enhancement, a model
introduced by Stoller et al for the separation of music vocals and accompaniment. This end …

Humans are able to localize objects in the environment using both visual and auditory cues,
integrating information from multiple modalities into a common reference frame. We …