The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

The performance of music source separation (MSS) models has been greatly improved in
recent years thanks to the development of novel neural network architectures and training …

Deep neural networks (DNN) techniques have become pervasive in domains such as
natural language processing and computer vision. They have achieved great success in …

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 …

A critical enabler for progress in neuromorphic computing research is the ability to
transparently evaluate different neuromorphic solutions on important tasks and to compare …

In reverberant conditions with multiple concurrent speakers, each microphone acquires a
mixture signal of multiple speakers at a different location. In over-determined conditions …

There is a surge in interest in self-supervised learning approaches for end-to-end speech
encoding in recent years as they have achieved great success. Especially, WavLM showed …

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 …

We present TokenSplit, a speech separation model that acts on discrete token sequences.
The model is trained on multiple tasks simultaneously: separate and transcribe each speech …

Self-supervised learning (SSL) methods such as WavLM have shown promising speech
separation (SS) results in small-scale simulation-based experiments. In this work, we extend …