Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model
書誌事項
- 公開日
- 2018-11
- 資源種別
- journal article
- DOI
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- 10.23919/apsipa.2018.8659577
- 10.48550/arxiv.1808.08056
- 公開者
- IEEE
説明
Independent low-rank matrix analysis (ILRMA) is a fast and stable method for blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models, which can only represent signals that follow a super-Gaussian distribution. In this paper, we focus on ILRMA based on a generalized Gaussian distribution (GGD-ILRMA) and propose a new type of GGD-ILRMA that adopts a time-variant sub-Gaussian distribution for the source model. By using a new update scheme called generalized iterative projection for homogeneous source models, we obtain a convergence-guaranteed update rule for demixing spatial parameters. In the experimental evaluation, we show the versatility of the proposed method, i.e., the proposed time-variant sub-Gaussian source model can be applied to various types of source signal.
8 pages, 5 figures, To appear in the Proceedings of APSIPA ASC 2018
収録刊行物
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- 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
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2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 1684-1691, 2018-11
IEEE
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キーワード
詳細情報 詳細情報について
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- CRID
- 1360004238944856448
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- 資料種別
- journal article
-
- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
