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 …

In this paper we look into the test methods to evaluate the quality of audio separation
algorithms. Specifically we try to correlate the results of listening tests with state-of-the-art …

Despite phenomenal progress in recent years, state-of-the-art music separation systems
produce source estimates with significant perceptual shortcomings, such as adding …

There is some uncertainty as to whether objective metrics for predicting the perceived quality
of audio source separation are sufficiently accurate. This issue was investigated by …

Music remixing is difficult when the original multitrack recording is not available. One
solution is to estimate the elements of a mixture using source separation. However, existing …

Multiple objective metrics are in use by the researchers to evaluate the performance of
separation systems. In this paper, we investigate the correlation of different state-of-the-art …

Quality of sound source separation algorithms has traditionally been evaluated based on a
set of established quality metrics: target distortion, interference from other sources, artifacts …

Supervised music source separation systems using deep learning are trained by minimizing
a loss function between pairs of predicted separations and ground-truth isolated sources …

Source separation evaluation is typically a top-down process, starting with perceptual
measures which capture fitness-for-purpose and followed by attempts to find physical …

Humans are able to distinguish between various sound sources in their environment and
selectively attend to specific ones. However, it is a difficult task to teach a computer to …