Convolutive Non-negative Matrix Factorisation with Sparseness Constraint
Discovering a parsimonious representation that reflects the structure of audio is a requirement of many machine learning and signal processing methods. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. We present an extension to NMF that is convolutive and forces a sparseness constraint. Combined with spectral magnitude analysis of audio, this method discovers auditory objects and their associated sparse activation patterns.