Nonnegative matrix factorisation for audio spectral unmixing

vendredi 9 mars 2018, 11h00 - 12h00

Salle du conseil, espace Turing

Data is often available in matrix form, in which columns are samples, and processing of such data often entails finding an approximate factorisation of the matrix in two factors. The first factor (the “dictionary”) yields recurring patterns characteristic of the data. The second factor (“the activation matrix”) describes in which proportions each data sample is made of these patterns. In the last 15 years, nonnegative matrix factorisation (NMF) has become a popular technique for analysing data with nonnegative values, with applications in many areas such as in text information retrieval, hyperspectral imaging or audio signal processing. The presentation will give an overview of NMF with a focus on majorisation-minimisation algorithms and will describe spectral unmixing applications in audio signal processing (source separation, denoising).