We propose a time-frequency processing method that localizes and enhances a target
sound by exploiting spectral and spatial characteristics of the ego-noise captured by a …

This article fills the gap between the growing interest in signal processing based on Deep
Neural Networks (DNN) and the new application of enhancing speech captured by …

Acoustic sensing from a multi-rotor drone is heavily degraded by the strong ego-noise
produced by the rotating motors and propellers. To address this problem, we propose a …

We propose an overdetermined source separation and localization method for a set of M
microphones distributed around an unknown number, N<; M, of sources. We reformulate the …

Sound source localization from a flying drone is a challenging task due to the strong ego-
noise from rotating motors and propellers as well as the movement of the drone and the …

When a micro aerial vehicle (MAV) captures sounds emitted by a ground or aerial source, its
motors and propellers are much closer to the microphone (s) than the sound source, thus …

Multiple moving sound source localization in real-world scenarios remains a challenging
issue due to interaction between sources, time-varying trajectories, distorted spatial cues …

In this paper, we present two approaches to localizing and tracking a sound source that
moves in a three-dimensional (3D) space. The sound signal was captured by a unique bi …

Multisource localization using time difference of arrival (TDOA) is challenging because the
correct combination of TDOA estimates across different microphone pairs, corresponding to …

This paper addresses the problem of multiple sound source counting and localization in
adverse acoustic environments, using microphone array recordings. The proposed time …