Independent low-rank matrix analysis based on complex student's t-distribution for blind audio source separation
書誌事項
- 公開日
- 2017-09
- 資源種別
- journal article
- DOI
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- 10.1109/mlsp.2017.8168129
- 10.48550/arxiv.1708.04795
- 公開者
- IEEE
説明
In this paper, we generalize a source generative model in a state-of-the-art blind source separation (BSS), independent low-rank matrix analysis (ILRMA). ILRMA is a unified method of frequency-domain independent component analysis and nonnegative matrix factorization and can provide better performance for audio BSS tasks. To further improve the performance and stability of the separation, we introduce an isotropic complex Student's $t$-distribution as a source generative model, which includes the isotropic complex Gaussian distribution used in conventional ILRMA. Experiments are conducted using both music and speech BSS tasks, and the results show the validity of the proposed method.
Preprint manuscript of 2017 IEEE International Workshop on Machine Learning for Signal Processing
収録刊行物
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- 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
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2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) 1-6, 2017-09
IEEE
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詳細情報 詳細情報について
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- CRID
- 1360004235338620544
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- 資料種別
- journal article
-
- データソース種別
-
- Crossref
- KAKEN
- OpenAIRE
