Guided source separation (GSS) is a type of target-speaker extraction method that relies on
pre-computed speaker activities and blind source separation to perform front-end …

We present Self-Remixing, a novel self-supervised speech separation method, which refines
a pre-trained separation model in an unsupervised manner. Self-Remixing consists of a …

A key challenge in machine learning is to generalize from training data to an application
domain of interest. This work extends the recently-proposed mixture invariant training (MixIT) …

In this work, we are interested in learning a model to separate sources that cannot be
recorded in isolation, such as parts of a machine that must run simultaneously in order for …

We propose a sound event localization and detection system based on a CNN-Conformer
base network. Our main contribution is to evaluate the use of pre-trained elements in this …

The performance of automatic speech recognition (ASR) systems severely degrades when
multi-talker speech overlap occurs. In meeting environments, speech separation is typically …

This paper describes an efficient unsupervised learning method for a neural source
separation model that utilizes a probabilistic generative model of observed multichannel …

This paper presents an unsupervised multichannel method that can separate moving sound
sources based on an amortized variational inference (AVI) of joint separation and …

Reverberation as supervision (RAS) is a framework that allows for training monaural speech
separation models from multi-channel mixtures in an unsupervised manner. In RAS, models …

Most existing direction-aware speech separation systems lead to performance degradation
when the angle difference between speakers is small due to the low spatial discrimination …