6. • 独立低ランク行列分析(ILRMA)[Kitamura+’16]
– 音源の低ランク性を仮定し、非負値行列因子分解
(NMF)により音源モデルを表現する
• 独立深層学習行列分析(IDLMA)[Mogami+’18]
– 多層ニューラルネットワークで各音源スペクトルの
分散への写像を学習する
• 多チャンネル変分自己符号化器法(MVAE)と
その高速アルゴリズム [Kameoka+’18, Li+’20, ’21]
– 条件付きVAE(CVAE)のデコーダ分布でスペクトロ
グラムの生成分布を学習する
6
周波数間の関係をモデリングする音源モデル
Time
Frequency
Basis
Frequency
Basis
Time
Frequency
Time
Frequency
Decoder
Frequency
Time
Time
Frequency
Frequency
Time
Frequency
Time
周波数ごとの音源分離とパーミュテーション整合の同時解決を可能となる
17. • [Smaragdis’98]: P. Smaragdis, “Blind separation of convolved mixtures in the frequency domain,” Neurocomputing, 22, pp.
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18. • [Nugraha+’20]: A. A. Nugraha, et al., “Flow-Based Independent Vector Analysis for Blind Source Separation,” IEEE SPL, 28, pp.
2173–2177, 2020.
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pp. 176-180, 2021
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pp. 601-605, 2020
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• [Ikeshita+22]: R. Ikeshita, et al., “ISS2: An Extension of Iterative Source Steering Algorithm for Majorization-Minimization-
Based Independent Vector Analysis”, arXiv: arXiv:2202.00875, 2022.
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19
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