非負値行列分解の確率的生成モデルと多チャネル音源分離への応用 (Generative model in nonnegative matrix facto...Daichi Kitamura
北村大地, "非負値行列分解の確率的生成モデルと多チャネル音源分離への応用," 慶應義塾大学理工学部電子工学科湯川研究室 招待講演, Kanagawa, November, 2015.
Daichi Kitamura, "Generative model in nonnegative matrix factorization and its application to multichannel sound source separation," Keio University, Science and Technology, Department of Electronics and Electrical Engineeing, Yukawa Laboratory, Invited Talk, Kanagawa, November, 2015.
非負値行列分解の確率的生成モデルと多チャネル音源分離への応用 (Generative model in nonnegative matrix facto...Daichi Kitamura
北村大地, "非負値行列分解の確率的生成モデルと多チャネル音源分離への応用," 慶應義塾大学理工学部電子工学科湯川研究室 招待講演, Kanagawa, November, 2015.
Daichi Kitamura, "Generative model in nonnegative matrix factorization and its application to multichannel sound source separation," Keio University, Science and Technology, Department of Electronics and Electrical Engineeing, Yukawa Laboratory, Invited Talk, Kanagawa, November, 2015.
Effective Optimization Algorithms for Blind and Supervised Music Source Separation with Nonnegative Matrix Factorization
長倉研究奨励賞第三次審査,20分間の研究概要説明
内容は自身の学位論文の一部に相当
独立性に基づくブラインド音源分離の発展と独立低ランク行列分析 History of independence-based blind source sep...Daichi Kitamura
東京大学 システム情報学専攻 談話会
2017年2月27日(月)15時~16時30分
北村大地, "独立性に基づくブラインド音源分離の発展と独立低ランク行列分析," 東京大学 システム情報学専攻 談話会, 2月27日, 2017年.
Daichi Kitamura, "History of independence-based blind source separation and independent low-rank matrix analysis," The University of Tokyo, Department of Information Physics and Computing, Seminar, 27th Feb., 2017.
独立低ランク行列分析に基づく音源分離とその発展(Audio source separation based on independent low-rank...Daichi Kitamura
北村大地, "独立低ランク行列分析に基づく音源分離とその発展," IEICE信号処理研究会, 2021年8月24日.
Daichi Kitamura, "Audio source separation based on independent low-rank matrix analysis and its extensions," IEICE Technical Group on Signal Processing, Aug. 24th, 2021.
http://d-kitamura.net
独立深層学習行列分析に基づく多チャネル音源分離(Multichannel audio source separation based on indepen...Daichi Kitamura
角野隼斗, 北村大地, 高宗典玄, 高道慎之介, 猿渡洋, 小野順貴, "独立深層学習行列分析に基づく多チャネル音源分離," 日本音響学会 2018年春季研究発表会講演論文集, 1-4-16, pp. 449–452, Saitama, March 2018.
Hayato Sumino, Daichi Kitamura, Norihiro Takamune, Shinnosuke Takamichi, Hiroshi Saruwatari, Nobutaka Ono, "Multichannel audio source separation based on independent deeply learned matrix analysis," Proceedings of 2018 Spring Meeting of Acoustical Society of Japan, 1-4-16, pp. 449–452, Saitama, March 2018 (in Japanese).
音源分離における音響モデリング(Acoustic modeling in audio source separation)Daichi Kitamura
北村大地, "音源分離における音響モデリング," 日本音響学会 サマーセミナー 招待講演, September 11th, 2017.
Daichi Kitamura, "Acoustic modeling in audio source separation," The Acoustical Society of Japan, Summer Seminar Invited Talk, September 11th, 2017.
The document describes a proposed hybrid method for multichannel signal separation using supervised nonnegative matrix factorization (SNMF). The method combines directional clustering for spatial separation with SNMF incorporating spectrogram restoration for spectral separation. Experiments show the hybrid method achieves better separation performance than conventional single-channel SNMF or multichannel NMF methods, as measured by signal-to-distortion ratio. The optimal divergence for the SNMF component involves a tradeoff between separation ability and ability to restore missing spectral components.
Effective Optimization Algorithms for Blind and Supervised Music Source Separation with Nonnegative Matrix Factorization
長倉研究奨励賞第三次審査,20分間の研究概要説明
内容は自身の学位論文の一部に相当
独立性に基づくブラインド音源分離の発展と独立低ランク行列分析 History of independence-based blind source sep...Daichi Kitamura
東京大学 システム情報学専攻 談話会
2017年2月27日(月)15時~16時30分
北村大地, "独立性に基づくブラインド音源分離の発展と独立低ランク行列分析," 東京大学 システム情報学専攻 談話会, 2月27日, 2017年.
Daichi Kitamura, "History of independence-based blind source separation and independent low-rank matrix analysis," The University of Tokyo, Department of Information Physics and Computing, Seminar, 27th Feb., 2017.
独立低ランク行列分析に基づく音源分離とその発展(Audio source separation based on independent low-rank...Daichi Kitamura
北村大地, "独立低ランク行列分析に基づく音源分離とその発展," IEICE信号処理研究会, 2021年8月24日.
Daichi Kitamura, "Audio source separation based on independent low-rank matrix analysis and its extensions," IEICE Technical Group on Signal Processing, Aug. 24th, 2021.
http://d-kitamura.net
独立深層学習行列分析に基づく多チャネル音源分離(Multichannel audio source separation based on indepen...Daichi Kitamura
角野隼斗, 北村大地, 高宗典玄, 高道慎之介, 猿渡洋, 小野順貴, "独立深層学習行列分析に基づく多チャネル音源分離," 日本音響学会 2018年春季研究発表会講演論文集, 1-4-16, pp. 449–452, Saitama, March 2018.
Hayato Sumino, Daichi Kitamura, Norihiro Takamune, Shinnosuke Takamichi, Hiroshi Saruwatari, Nobutaka Ono, "Multichannel audio source separation based on independent deeply learned matrix analysis," Proceedings of 2018 Spring Meeting of Acoustical Society of Japan, 1-4-16, pp. 449–452, Saitama, March 2018 (in Japanese).
音源分離における音響モデリング(Acoustic modeling in audio source separation)Daichi Kitamura
北村大地, "音源分離における音響モデリング," 日本音響学会 サマーセミナー 招待講演, September 11th, 2017.
Daichi Kitamura, "Acoustic modeling in audio source separation," The Acoustical Society of Japan, Summer Seminar Invited Talk, September 11th, 2017.
The document describes a proposed hybrid method for multichannel signal separation using supervised nonnegative matrix factorization (SNMF). The method combines directional clustering for spatial separation with SNMF incorporating spectrogram restoration for spectral separation. Experiments show the hybrid method achieves better separation performance than conventional single-channel SNMF or multichannel NMF methods, as measured by signal-to-distortion ratio. The optimal divergence for the SNMF component involves a tradeoff between separation ability and ability to restore missing spectral components.
This document proposes a flexible microphone array system using informed source separation methods for a rescue robot. It aims to detect victim speech in disaster areas using multiple microphones on the robot's flexible body. The proposed method uses supervised rank-1 nonnegative matrix factorization (NMF) and statistical signal estimation to address two key problems: ego-noise basis mismatch due to the robot's self-vibrations, and speech model ambiguity. Experiments show the proposed approach outperforms conventional independent vector analysis and single-channel NMF, improving speech detection even with mismatched ego-noise recordings.
This document summarizes a research talk on statistical-model-based speech enhancement techniques that aim to reduce noise without generating musical noise artifacts. The talk outlines conventional enhancement methods like spectral subtraction and Wiener filtering that often cause musical noise. It then proposes a biased minimum mean-square error estimator that can achieve a musical-noise-free state by introducing a bias parameter. Analysis and experiments show this method can reduce noise while keeping the kurtosis ratio fixed at 1.0 to prevent musical noise, outperforming other techniques in terms of speech quality. A strong speech prior model is found to limit achieving musical-noise-free states, so the prior must be carefully selected.
The document proposes an improved method for audio signal separation using supervised nonnegative matrix factorization (NMF) with time-variant basis deformation. The key contributions are:
1. Classifying supervised bases into time-variant attack and sustain parts and applying different all-pole model-based deformations to each.
2. Introducing discriminative training to avoid overfitting the interference signal and better separate the target.
3. An iterative approximated algorithm is presented that searches for deformation matrices representing the target signal while being constrained to also fit the mixture signal.
4. Experimental results on instrument mixtures show the proposed method achieves better signal-to-distortion ratio performance than previous supervised NMF techniques.
Shoichi Koyama, Naoki Murata, and Hiroshi Saruwatari. "Super-resolution in sound field recording and reproduction based on sparse representation"
presented at 5th Joint Meeting Acoustical Society of America and Acoustical Society of Japan (28 Nov. - 2 Dec. 2016, Honolulu, USA)
Shoichi Koyama, "Source-Location-Informed Sound Field Recording and Reproduction: A Generalization to Arrays of Arbitrary Geometry"
Presented in 2016 AES International Conference on Sound Field Control (July 18-20 2016, Guildford, UK)
18. 低ランク性を導入した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)を低ランク非負
値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に対応する音源モデルを活用 19
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 et al., ICASSP2015, IEEE-Trans.2016]