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 …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

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 …

An important problem in ad-hoc microphone speech separation is how to guarantee the
robustness of a system with respect to the locations and numbers of microphones. The …

The external language models (LM) integration remains a challenging task for end-to-end
(E2E) automatic speech recognition (ASR) which has no clear division between acoustic …

Beamforming has been extensively investigated for multi-channel audio processing tasks.
Recently, learning-based beamforming methods, sometimes called neural beamformers …

As the most widely-used spatial filtering approach for multi-channel speech separation,
beamforming extracts the target speech signal arriving from a specific direction. An …

We propose a novel adversarial multi-task learning scheme, aiming at actively curtailing the
inter-talker feature variability while maximizing its senone discriminability so as to enhance …

The teacher-student (T/S) learning has been shown to be effective for a variety of problems
such as domain adaptation and model compression. One shortcoming of the T/S learning is …

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence
(AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented …