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統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析– Blind source separation based on statistical independence and low-rank matrix decomposition –Independent low-rank matrix analysis–
北村大地, "統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析–," 筑波大学システム情報工学研究科マルチメディア研究室 招待講演, Ibaraki, September 26th, 2016.
Daichi Kitamura, "Blind source separation based on statistical independence and low-rank matrix decomposition –Independent low-rank matrix analysis–," University of Tsukuba, Graduate School of Systems and Information Engineering, Multimedia Laboratory, Invited Talk, Ibaraki, September 26th, 2016.
Assistant Professor
at
National Institute of Technology, Kagawa College
北村大地, "統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析–," 筑波大学システム情報工学研究科マルチメディア研究室 招待講演, Ibaraki, September 26th, 2016.
Daichi Kitamura, "Blind source separation based on statistical independence and low-rank matrix decomposition –Independent low-rank matrix analysis–," University of Tsukuba, Graduate School of Systems and Information Engineering, Multimedia Laboratory, Invited Talk, Ibaraki, September 26th, 2016.
Similar to 統計的独立性と低ランク行列分解理論に基づくブラインド音源分離 –独立低ランク行列分析– Blind source separation based on statistical independence and low-rank matrix decomposition –Independent low-rank matrix analysis–
18.
IVAとNMFを融合した新しいBSS(2016~)
• 独立低ランク行列分析(independent low-rank matrix analysis: ILRMA)
– 独立音源の詳細な時間周波数構造(のパワー)を低ランク行列
として捉えながら線形分離フィルタを学習
– 板倉斎藤擬距離基準NMFの事前分布を活用 18
濃淡は分散値
(信号のパワー)
Frequency
Time
Frequency
Time
Frequency
Time
Frequency
Basis
Basis
Time
IVAの
音源モデル
ILRMAの
音源モデル
全周波数で共通の
分散をもつ音源モデル
時間周波数で分散が
変動する音源モデル
基底数は任意
[Kitamura, 2015], [Kitamura, 2016]
23.
IVAとNMFを融合した新しいBSS(2016~)
• 独立低ランク行列分析(independent low-rank matrix analysis: ILRMA)
– 但し,IVAは共通分散だがスケールは周波数毎に不定なので,
フラットなスペクトルだけではなく任意の形状を表現可能 23
濃淡は分散値
(信号のパワー)
Frequency
Time
Frequency
Time
Frequency
Time
Frequency
Basis
Basis
Time
IVAの
音源モデル
ILRMAの
音源モデル
全周波数で共通の
分散をもつ音源モデル
時間周波数で分散が
変動する音源モデル
基底数は任意
(再掲)
54.
参考文献(1/3)
• [Lee, 1999]: D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix
factorization,” Nature, vol. 401, pp. 788–791, 1999.
• [Lee, 2000]: D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in
Proc. Adv. Neural Inform. Process. Syst., 2000, vol. 13, pp. 556–562.
• [Smaragdis, 1998]: P. Smaragdis, “Blind separation of convolved mixtures in the frequency
domain,” Neurocomputing, vol. 22, pp. 21–34, 1998.
• [Sawada, 2004]: H. Sawada, R. Mukai, S. Araki, and S.Makino, “Convolutive blind source
separation for more than two sources in the frequency domain,” in Proc. IEEE Int. Conf.
Acoust., Speech, Signal Process., 2004, pp. III-885–III-888.
• [Saruwatari, 2006]: H. Saruwatari, T. Kawamura, T. Nishikawa, A. Lee, and K. Shikano, “Blind
source separation based on a fast-convergence algorithm combining ICA and beamforming,”
IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 2, pp. 666–678, Mar. 2006.
• [Araki, 2003]: S. Araki, S. Makino, Y. Hinamoto, R. Mukai, T. Nishikawa, and H. Saruwatari,
“Equivalence between frequency-domain blind source separation and frequency-domain
adaptive beamforming for convolutive mixtures,” EURASIP Journal on Advances in Signal
Process., vol. 2003, no. 11, pp. 1–10, 2003.
• [Hiroe, 2006]: A. Hiroe, “Solution of permutation problem in frequency domain ICA using
multivariate probability density functions,” in Proc. Int. Conf. Independent Compon. Anal. Blind
Source Separation, 2006, pp. 601–608.
54
55.
参考文献(2/3)
• [Kim, 2006]: T. Kim, T. Eltoft, and T.-W. Lee, “Independent vector analysis: An extension of
ICA to multivariate components,” in Proc. Int. Conf. Independent Compon. Anal. Blind Source
Separation, 2006, pp. 165–172.
• [Kim, 2007]: T. Kim, H. T. Attias, S.-Y. Lee, and T.-W. Lee, “Blind source separation exploiting
higher-order frequency dependencies,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15,
no. 1, pp. 70–79, 2007.
• [Kitamura, 2015]: D. Kitamura, N. Ono, H. Sawada, H. Kameoka, and H. Saruwatari, “Efficient
multichannel nonnegative matrix factorization exploiting rank-1 spatial model,” in Proc. IEEE
Int. Conf. Acoust., Speech, Signal Process., 2015, pp. 276–280.
• [Kitamura, 2016]: D. Kitamura, H. Saruwatari, H. Kameoka, Y. Takahashi, K. Kondo and S.
Nakamura, “Determined blind source separation unifying independent vector analysis and
nonnegative matrix factorization,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 24,
no. 9, pp. 1626–1641, Spt. 2016.
• [Févotte, 2009]: C. Févotte, N. Bertin, and J.-L.Durrieu, “Nonnegative matrix factorization with
the Itakura-Saito divergence. With application to music analysis,” Neural Comput., vol. 21, no.
3, pp. 793–830, 2009.
• [Sawada, 2013]: H. Sawada, H.Kameoka, S.Araki, and N. Ueda, “Multichannel extensions of
non-negative matrix factorization with complex-valued data,” IEEE Trans. Audio, Speech,
Lang. Process., vol. 21, no. 5, pp. 971–982, May 2013.
55
56.
参考文献(3/3)
• [Duong, 2010]: N. Q. K. Duong, E. Vincent, and R. Gribonval, “Under-determined reverberant
audio source separation using a full-rank spatial covariance model,” IEEE Trans. Audio,
Speech, Lang. Process., vol. 18, no. 7, pp. 1830–1840, Sep. 2010.
• [Kitamura, 2015]: D. Kitamura, N. Ono, H. Sawada, H. Kameoka, and H. Saruwatari,
“Relaxation of rank-1 spatial constraint in overdetermined blind source separation,” in Proc.
Eur. Signal Process. Conf., 2015, pp. 1271–1275.
56