Supervised Audio Source Separation Based on Nonnegative Matrix Factorization with Cosine Similarity Penalty
In this study, we aim to improve the performance of audio source separation for monaural mixture signals. For monaural audio source separation, semisupervised nonnegative matrix factorization (SNMF) can achieve higher separation performance by employing small supervised signals. In particular, penal...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electronics Information Communication Engineers
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |