Stationary/transient audio separation using convolutional autoencoders
Gerard Roma, Owen Green, Pierre Alexandre Tremblay · Huddersfield Research Portal (University of Huddersfield) · 2018
Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content pro- duction. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsuper- vised algorithms for individual and and joint separation. We de- scribe our implementation and show examples. Our results show promise for the use of convolutional autoencoders in the extraction of sparse components from audio spectrograms, particularly using monophonic sounds.