The understanding of the surrounding environment plays a critical role in autonomous
robotic systems, such as self-driving cars. Extensive research has been carried out …

In various signal processing applications, such as audio signal recovery, the extraction of
desired signals from a mixture of other signals is a crucial task. To achieve superior …

In this paper, we propose a multi-channel speech source separation method with a deep
neural network (DNN) which is trained under the condition that no clean signal is available …

With the growth of acoustic data in the development of multimedia tools, mobile phones and
the Internet of Multimedia Things (IoMT), recent studies exploit different models of machine …

Mentoring-reverse mentoring, which is a novel knowledge transfer framework for
unsupervised learning, is introduced in multi-channel speech source separation. This …

This study presents a framework to enable efficient and stable adversarial learning of
unsupervised multichannel source separation models. When the paired data, ie, the mixture …

Acquiring clear acoustic signals is critical for the analysis of animal vocalizations.
Bioacoustics studies commonly face the problem of overlapping signals, which can impede …

Recently, deep neural networks (DNNs) with multiple bidirectional long short term memory
(BLSTM) layers have been successfully applied to supervised multi-channel speech …

Source separation aims at decomposing a vector into additive components. This is often
done by first estimating source parameters before feeding them into a filtering method, often …

Background and environmental noises negatively affect the quality of verbal communication
between humans as well as in human-computer interaction. However, this problem is …