Title: End-to-End Sound Source Separation Conditioned On Instrument Labels,
Abstract: Can we perform an end-to-end sound source separation (SSS) with a variable
number of sources using a deep learning model? This paper presents an extension
of the Wave-U-Net model which allows end-to-end monaural source separation with
a non-fixed number of sources. Furthermore, we propose multiplicative
conditioning with instrument labels at the bottleneck of the Wave-U-Net and
show its effect on the separation results. This approach can be further
extended to other types of conditioning such as audio-visual SSS and
score-informed SSS.