6533b81ffe1ef96bd1278663
RESEARCH PRODUCT
Ray-Space-Based Multichannel Nonnegative Matrix Factorization for Audio Source Separation
Augusto SartiJ.j. Carabias-ortiFabio AntonacciMirco PezzoliMaximo Cobossubject
Covariance functionComputer scienceApplied Mathematics020206 networking & telecommunications02 engineering and technologyExtension (predicate logic)Mixture modelMatrix decompositionNon-negative matrix factorizationTime–frequency analysisblind source separationSignal Processing0202 electrical engineering electronic engineering information engineeringSource separationNon -negative matrix factorization (NMF)array signal processingElectrical and Electronic EngineeringAlgorithmdescription
Nonnegative matrix factorization (NMF) has been traditionally considered a promising approach for audio source separation. While standard NMF is only suited for single-channel mixtures, extensions to consider multi-channel data have been also proposed. Among the most popular alternatives, multichannel NMF (MNMF) and further derivations based on constrained spatial covariance models have been successfully employed to separate multi-microphone convolutive mixtures. This letter proposes a MNMF extension by considering a mixture model with Ray-Space-transformed signals, where magnitude data successfully encodes source locations as frequency-independent linear patterns. We show that the MNMF algorithm can be seamlessly adapted to consider Ray-Space-transformed data, providing competitive results with recent state-of-the-art MNMF algorithms in a number of configurations using real recordings.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 | IEEE Signal Processing Letters |