Animal vocalisations and natural soundscapes are fascinating objects of study, and contain
valuable evidence about animal behaviours, populations and ecosystems. They are studied …

Single-channel, speaker-independent speech separation methods have recently seen great
progress. However, the accuracy, latency, and computational cost of such methods remain …

Models for audio source separation usually operate on the magnitude spectrum, which
ignores phase information and makes separation performance dependant on hyper …

We present and release a new tool for music source separation with pre-trained models
called Spleeter. Spleeter was designed with ease of use, separation performance, and …

Popular music is often composed of an accompaniment and a lead component, the latter
typically consisting of vocals. Filtering such mixtures to extract one or both components has …

Source separation models either work on the spectrogram or waveform domain. In this work,
we show how to perform end-to-end hybrid source separation, letting the model decide …

Time-frequency (TF) domain masking is a mainstream approach for single-channel speech
enhancement. Recently, focuses have been put to phase prediction in addition to amplitude …

Most deep learning-based models for speech enhancement have mainly focused on
estimating the magnitude of spectrogram while reusing the phase from noisy speech for …

Source separation for music is the task of isolating contributions, or stems, from different
instruments recorded individually and arranged together to form a song. Such components …

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 …