Download Stationary/transient Audio Separation Using Convolutional Autoencoders 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.
Download Audio Morphing Using Matrix Decomposition and Optimal Transport This paper presents a system for morphing between audio recordings in a continuous parameter space.
The proposed approach
combines matrix decompositions used for audio source separation with displacement interpolation enabled by 1D optimal transport. By interpolating the spectral components obtained using nonnegative matrix factorization of the source and target signals, the
system allows varying the timbre of a sound in real time, while
maintaining its temporal structure. Using harmonic / percussive
source separation as a pre-processing step, the system affords more
detailed control of the interpolation in perceptually meaningful dimensions.