Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CNMF
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Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | IEEE International Workshop on Multimedia Signal Processing (MMSP) |
| Tila | Hyväksytty/In press - 2020 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE International Workshop on Multimedia Signal Processing - Kesto: 1 tammikuuta 1900 → … |
Conference
| Conference | IEEE International Workshop on Multimedia Signal Processing |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
This work addresses the problem of multichannel source separation combining two powerful approaches, multichannel spectral factorization with recent monophonic deep-learning (DL) based spectrum inference. Individual source spectra at different channels are estimated with a Masker-Denoiser Twin Network (MaD TwinNet), able to model long-term temporal patterns of a musical piece. The monophonic source spectrograms are used within a spatial covariance mixing model based on Complex Non-Negative Matrix Factorization (CNMF) that predicts the spatial characteristics of each source. The proposed framework is evaluated on the task of singing voice separation with a large multichannel dataset. Experimental results show that our joint DL+CNMF method outperforms both the individual monophonic DL-based separation and the multichannel CNMF baseline methods.