This paper reports on the design and outcomes of the 2nd Clarity Enhancement Challenge
(CEC2), a challenge for stimulating novel approaches to hearing-aid speech intelligibility …

We present RemixIT, a simple yet effective self-supervised method for training speech
enhancement without the need of a single isolated in-domain speech nor a noise waveform …

Transformers have been the most successful architecture for various speech modeling tasks,
including speech separation. However, the self-attention mechanism in transformers with …

Supervised speech enhancement models are trained using artificially generated mixtures of
clean speech and noise signals, which may not match real-world recording conditions at test …

While recent developments on the design of neural networks have greatly advanced the
state-of-the-art of speech enhancement and separation systems, practical applications of …

Universal source separation (USS) is a fundamental research task for computational
auditory scene analysis, which aims to separate mono recordings into individual source …

We introduce a new paradigm for single-channel target source separation where the
sources of interest can be distinguished using non-mutually exclusive concepts (eg …

Simple Summary Automatic bird sound recognition using artificial intelligence technology
has been widely used to identify bird species recently. However, the bird sounds recorded in …

We propose FedEnhance, an unsupervised federated learning (FL) approach for speech
enhancement and separation with non-IID distributed data across multiple clients. We …

Speech separation remains an important topic for multispeaker technology researchers.
Convolution augmented transformers (conformers) have performed well for many speech …