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 …

Music source separation is the task of decomposing music into its constitutive components,
eg, yielding separated stems for the vocals, bass, and drums. Such a separation has many …

Spectral decomposition by nonnegative matrix factorisation (NMF) has become state-of-the-
art practice in many audio signal processing tasks, such as source separation, enhancement …

Speech enhancement and separation are core problems in audio signal processing, with
commercial applications in devices as diverse as mobile phones, conference call systems …

We evaluate some recent developments in recurrent neural network (RNN) based speech
enhancement in the light of noise-robust automatic speech recognition (ASR). The proposed …

Model-based methods and deep neural networks have both been tremendously successful
paradigms in machine learning. In model-based methods, problem domain knowledge can …

This article addresses the problem of multichannel audio source separation. We propose a
framework where deep neural networks (DNNs) are used to model the source spectra and …

This paper deals with the separation of music into individual instrument tracks which is
known to be a challenging problem. We describe two different deep neural network …

Formulating speech separation as a binary classification problem has been shown to be
effective. While good separation performance is achieved in matched test conditions using …

Automatic music transcription is considered by many to be a key enabling technology in
music signal processing. However, the performance of transcription systems is still …