1) The document discusses a semi-supervised nonnegative matrix factorization method with a cosine penalty condition for audio source separation.
2) It proposes adding a cosine similarity penalty term to penalize similarity between basis matrices, to improve on existing penalized SNMF methods.
3) Experiments show the proposed method achieves higher source separation performance compared to existing methods, measured by average and median SDR values, but the optimal weight coefficient values are peaky.
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.
北村大地, 小野順貴, "独立性基準を用いた非負値行列因子分解の効果的な初期値決定法," 日本音響学会 2016年春季研究発表会, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
1) The document discusses a semi-supervised nonnegative matrix factorization method with a cosine penalty condition for audio source separation.
2) It proposes adding a cosine similarity penalty term to penalize similarity between basis matrices, to improve on existing penalized SNMF methods.
3) Experiments show the proposed method achieves higher source separation performance compared to existing methods, measured by average and median SDR values, but the optimal weight coefficient values are peaky.
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.
北村大地, 小野順貴, "独立性基準を用いた非負値行列因子分解の効果的な初期値決定法," 日本音響学会 2016年春季研究発表会, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
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).
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...Daichi Kitamura
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.
ICASSP 2019音声&音響論文読み会(https://connpass.com/event/128527/)での発表資料です。
AASP (Audio and Acoustic Signal Processing) 分野の紹介と、ICASSP 2019での動向を紹介しています。#icassp2019jp
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.
The document appears to be a research paper discussing anomaly detection in time series data using a machine learning model. It includes the following:
1. A description of an experimental setup using miniature machine sound data to classify normal vs anomalous sounds with varying signal-to-noise ratios.
2. Results showing the area under the receiver operating characteristic curve (AUC) scores for the model under different noise levels, with AUC scores ranging from 0.5 to 0.9.
3. A graph plotting the anomaly-to-normal ratio against the AUC scores for different noise levels, suggesting higher performance with more anomalous samples.
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).
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...Daichi Kitamura
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.
ICASSP 2019音声&音響論文読み会(https://connpass.com/event/128527/)での発表資料です。
AASP (Audio and Acoustic Signal Processing) 分野の紹介と、ICASSP 2019での動向を紹介しています。#icassp2019jp
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.
The document appears to be a research paper discussing anomaly detection in time series data using a machine learning model. It includes the following:
1. A description of an experimental setup using miniature machine sound data to classify normal vs anomalous sounds with varying signal-to-noise ratios.
2. Results showing the area under the receiver operating characteristic curve (AUC) scores for the model under different noise levels, with AUC scores ranging from 0.5 to 0.9.
3. A graph plotting the anomaly-to-normal ratio against the AUC scores for different noise levels, suggesting higher performance with more anomalous samples.
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
Amplitude spectrogram prediction from mel-frequency cepstrum coefficients and...Kitamura Laboratory
Shoya Kawaguchi and Daichi Kitamura,
"Amplitude spectrogram prediction from mel-frequency cepstrum coefficients and loudness using deep neural networks,"
Proceedings of RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP 2023), pp. 225–228, Honolulu, USA, March 2023.
Heart rate estimation of car driver using radar sensors and blind source sepa...Kitamura Laboratory
Keito Murata, Daichi Kitamura, Ryo Saito, and Daichi Ueki,
"Heart rate estimation of car driver using radar sensors and blind source separation,"
Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2022), pp. 1157–1164, Chiang Mai, Thailand, November 2022.
DNN-based frequency-domain permutation solver for multichannel audio source s...Kitamura Laboratory
Fumiya Hasuike, Daichi Kitamura, and Rui Watanabe,"DNN-based frequency-domain permutation solver for multichannel audio source separation," Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2022), pp. 872–877, Chiang Mai, Thailand, November 2022.
Linear multichannel blind source separation based on time-frequency mask obta...Kitamura Laboratory
This document proposes a new method for linear multichannel blind source separation (BSS) based on time-frequency masks obtained from harmonic/percussive sound separation (HPSS). The proposed method applies HPSS independently to temporarily estimated sources to generate harmonic and percussive masks, then smooths the masks and uses them in time-frequency masking-based BSS. Experiments show the proposed method achieves higher source separation quality than single-channel HPSS and outperforms other multichannel BSS methods, demonstrating the effectiveness of integrating HPSS with multichannel BSS.
Prior distribution design for music bleeding-sound reduction based on nonnega...Kitamura Laboratory
Yusaku Mizobuchi, Daichi Kitamura, Tomohiko Nakamura, Hiroshi Saruwatari, Yu Takahashi, and Kazunobu Kondo, "Prior distribution design for music bleeding-sound reduction based on nonnegative matrix factorization," Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2021), pp. 651–658, Tokyo, Japan, December 2021.
Blind audio source separation based on time-frequency structure modelsKitamura Laboratory
Daichi Kitamura, "Blind audio source separation based on time-frequency structure models," Invited Overview Session in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2021), Tokyo, Japan, December 2021.
1) The document proposes a new metric to predict the accuracy of source separation by independent component analysis (ICA) using finite sample data, as directly calculating independence from theoretical distributions is not possible with limited samples.
2) An experiment shows high correlation (0.97) between the proposed metric, which calculates the squared error between sample expectations of signal sources, and actual ICA separation accuracy, allowing prediction of ICA performance before application.
3) The metric improves on existing metrics like symmetric uncertainty coefficient that rely on approximating distributions from finite bins, and enables advance assessment of ICA feasibility for problems involving mixing of multiple signal sources observed through limited sensor data.
This document discusses independent low-rank matrix analysis (ILRMA) for blind source separation of multichannel audio signals. ILRMA introduces a low-rank source model in addition to maximizing statistical independence between sources, using an iterative optimization algorithm. ILRMA is one of the latest blind source separation techniques, building upon prior methods like independent component analysis (ICA), frequency-domain ICA, and independent vector analysis.