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Music Signal Separation Combining Directional Clustering and Nonnegative Matrix Factorization with Spectrogram Restoration ∗
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kitamura, Daichi |
| Copyright Year | 2014 |
| Abstract | In this thesis, to address a music signal separation problem, I propose a new hybrid method that concatenates directional clustering and supervised nonnegative matrix factorization (NMF) with spectrogram restoration for the purpose of the specific sound extraction from the multichannel music signal that consists of multiple instrumental sounds. Recently, a main format for obtaining musical tunes has become electronic data such as music files, which can be made available over the Internet owing to progress in information technology. Hence, users can easily obtain and edit music tunes, resulting in the active creation of new contents. According to this background, music signal separation technologies have much attention. Music signal separation is aimed to extract a specific target signal from music signals that contain multiple music instrumental sounds. Audio remixing by the users, automatic music transcription, and musical instrument education are one of the feasible music signal separation applications. In the previous studies, music signal separation based on NMF has been a very active area of the research. Various methods using NMF have been proposed, but they remain many problems, e.g., poor convergence in update rules in NMF and lack of robustness. To solve these problems, I propose a new supervised NMF (SNMF) with spectrogram restoration and its hybrid method that concatenates the proposed SNMF after directional clustering. Via extrapolation of supervised spectral bases, this SNMF with spectrogram restoration attempts both target ∗Master’s Thesis, Department of Information Science, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-MT1251035, March 6, 2014. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://researchmap.jp/?action=cv_download_main&upload_id=62012 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
