This study introduces a progressive neural network (PNN) model for direction of arrival
(DOA) estimation, DOA-PNN, addressing the challenge due to catastrophic forgetting in …

Extracting direct-path spatial feature is crucial for sound source localization in adverse
acoustic environments. This paper proposes IPDnet, a neural network that estimates direct …

Abstract Sound Source Localization (SSL) involves estimating the Direction of Arrival (DOA)
of sound sources. Since the DOA estimation output space is continuous, regression might be …

Multi-dimensional speaker localization (SL) aims to estimate the two-or three-dimensional
locations of speakers. A recent advancement in multi-dimensional SL is the end-to-end deep …

Due to their robustness and flexibility, neural-driven beamformers are a popular choice for
speech separation in challenging environments with a varying amount of simultaneous …

Polyphonic sound source localization and detection (SSLD) task aims to recognize the
categories of sound events, identify their onset and offset times, and detect their …

In recent years, target speech separation has drawn a lot of attention with the development
of deep-learning methods. The target speech from the specific direction-of-interest (DOI) can …

Deep neural network (DNN) has made impressive progress in sound source localization
(SSL) tasks with the hard n-hot labels that represent specific directions-of-arrivals (DOAs) …