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 …

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 …

The cocktail party problem, ie, tracing and recognizing the speech of a specific speaker
when multiple speakers talk simultaneously, is one of the critical problems yet to be solved …

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 …

The recently-proposed deep clustering algorithm represents a fundamental advance
towards solving the cocktail party problem in the single-channel case. When multiple …

This work proposes a neural network to extensively exploit spatial information for
multichannel joint speech separation, denoising and dereverberation, named SpatialNet. In …

The ability to localize and track acoustic events is a fundamental prerequisite for equip**
machines with the ability to be aware of and engage with humans in their surrounding …

In recent years, the Internet of Things (IoT) has grown to include the tracking of devices
through the use of indoor positioning systems (IPSs) and location-based services (LBSs) …