Singular Spectrum Analysis for Source Separation in Drone-Based Audio Recording

Blind Source Separation from Single Measurements Using Singular Spectrum Analysis

Singular Spectrum Analysis (SSA) is a powerful data decomposition/recompostion technique that can be used to reduce the noise in time series. Compared to existing solutions aiming at similar purposes, such as frequency-based filtering, it benefits from easier-to-exploit intuitions, applicability in contexts where low sampling rates make standard frequency analyses challenging, and the (theoreti...

Deep neural network based audio source separation

Audio source separation aims to extract individual sources from mixtures of multiple sound sources. Many techniques have been developed such as independent component analysis, computational auditory scene analysis, and non-negative matrix factorisation. A method based on Deep Neural Networks (DNNs) and time-frequency (T-F) masking has been recently developed for binaural audio source separation...

Unsupervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning

Separating audio mixtures into individual tracks has been a long standing challenging task. We introduce a novel unsupervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover,...

Development of Independent Component Analysis Based Blind Source Separation Algorithms for Audio /Image Separation

Bayesian audio source separation

In this chapter we describe a Bayesian approach to audio source separation. The approach relies on probabilistic modeling of sound sources as (sparse) linear combinations of atoms from a dictionary and Markov chain Monte Carlo (MCMC) inference. Several prior distributions are considered for the source expansion coefficients. We first consider independent and identically distributed (iid) genera...