Stationary/transient Audio Separation Using Convolutional Autoencoders

Gerard Roma; Owen Green; Pierre Alexandre Tremblay
DAFx-2018 - Aveiro
Extraction of stationary and transient components from audio has many potential applications to audio effects for audio content production. In this paper we explore stationary/transient separation using convolutional autoencoders. We propose two novel unsupervised algorithms for individual and and joint separation. We describe 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.