Consistent independent low-rank matrix analysis for determined blind source separation
Abstract Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power sp...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-11-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
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| Online Access: | http://link.springer.com/article/10.1186/s13634-020-00704-4 |
| _version_ | 1818345997545242624 |
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| author | Daichi Kitamura Kohei Yatabe |
| author_facet | Daichi Kitamura Kohei Yatabe |
| author_sort | Daichi Kitamura |
| collection | DOAJ |
| description | Abstract Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such a highly developed source model can solve the permutation problem of the frequency-domain BSS to a large extent, which is the reason for the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrograms, which is called consistency, and hence, we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related to each other via the uncertainty principle. Such co-occurrence among the spectral components can function as an assistant for solving the permutation problem, which has been demonstrated by a recent study. On the basis of these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively evaluated through experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA that include some topics not fully discussed in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system. |
| first_indexed | 2024-12-13T17:11:16Z |
| format | Article |
| id | doaj.art-77314d40d0a6460781fcdbc03a930ba6 |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-13T17:11:16Z |
| publishDate | 2020-11-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-77314d40d0a6460781fcdbc03a930ba62022-12-21T23:37:33ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802020-11-012020113510.1186/s13634-020-00704-4Consistent independent low-rank matrix analysis for determined blind source separationDaichi Kitamura0Kohei Yatabe1National Institute of Technology, Kagawa CollegeWaseda UniversityAbstract Independent low-rank matrix analysis (ILRMA) is the state-of-the-art algorithm for blind source separation (BSS) in the determined situation (the number of microphones is greater than or equal to that of source signals). ILRMA achieves a great separation performance by modeling the power spectrograms of the source signals via the nonnegative matrix factorization (NMF). Such a highly developed source model can solve the permutation problem of the frequency-domain BSS to a large extent, which is the reason for the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrograms, which is called consistency, and hence, we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other. In other words, the time-frequency components are related to each other via the uncertainty principle. Such co-occurrence among the spectral components can function as an assistant for solving the permutation problem, which has been demonstrated by a recent study. On the basis of these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm. Its performance was extensively evaluated through experiments performed with various window lengths and shift lengths. The results indicated several tendencies of the original and proposed ILRMA that include some topics not fully discussed in the literature. For example, the proposed Consistent ILRMA tends to outperform the original ILRMA when the window length is sufficiently long compared to the reverberation time of the mixing system.http://link.springer.com/article/10.1186/s13634-020-00704-4Audio source separationConvolutive mixtureDemixing filter estimationPhase-aware signal processingSpectrogram consistency |
| spellingShingle | Daichi Kitamura Kohei Yatabe Consistent independent low-rank matrix analysis for determined blind source separation EURASIP Journal on Advances in Signal Processing Audio source separation Convolutive mixture Demixing filter estimation Phase-aware signal processing Spectrogram consistency |
| title | Consistent independent low-rank matrix analysis for determined blind source separation |
| title_full | Consistent independent low-rank matrix analysis for determined blind source separation |
| title_fullStr | Consistent independent low-rank matrix analysis for determined blind source separation |
| title_full_unstemmed | Consistent independent low-rank matrix analysis for determined blind source separation |
| title_short | Consistent independent low-rank matrix analysis for determined blind source separation |
| title_sort | consistent independent low rank matrix analysis for determined blind source separation |
| topic | Audio source separation Convolutive mixture Demixing filter estimation Phase-aware signal processing Spectrogram consistency |
| url | http://link.springer.com/article/10.1186/s13634-020-00704-4 |
| work_keys_str_mv | AT daichikitamura consistentindependentlowrankmatrixanalysisfordeterminedblindsourceseparation AT koheiyatabe consistentindependentlowrankmatrixanalysisfordeterminedblindsourceseparation |