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The Journal of Supercomputing

Erschienen in:

06.04.2021

Parallel multichannel blind source separation using a spatial covariance model and nonnegative matrix factorization

verfasst von: A. J. Muñoz-Montoro, J. J. Carabias-Orti, R. Cortina, S. García-Galán, J. Ranilla

Erschienen in: The Journal of Supercomputing | Ausgabe 10/2021

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Abstract

In this paper, we present a multichannel nonnegative matrix factorization (MNMF) system for the task of source separation. We propose a novel signal model using spatial covariance matrices (SCM) where the mixing filter encodes the spatial information and the source variances are modeled using a NMF structure. Moreover, the proposed model is initialized with the estimated source direction of arrival (DoA) in order to mitigate the strong sensitivity to parameter initialization. The proposed system has been evaluated for the task of music source separation using a multichannel classical chamber music dataset showing that it is possible to reach real time in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques.

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RT\(_{60}\) is the time required for reflections of a direct sound to decay by 60 dB below the level of the direct sound.

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Campbell DR, Palomaki KJ, Brown G (2005) A MATLAB simulation of “shoebox’’ room acoustics for use in research and teaching. Comput Inf Syst 9:48–51

Canadas-Quesada F, Fitzgerald D, Vera-Candeas P, Ruiz-Reyes N (2017) Harmonic-percussive sound separation using rhythmic information from non-negative matrix factorization in single-channel music recordings. DAFx 2017 - Proceedings of the 20th International Conference on Digital Audio Effects (i), 276–282

Carabias-Orti JJ, Nikunen J, Virtanen T, Vera-Candeas P (2018) Multichannel blind Sound source separation using spatial covariance model With level and time Differences and nonnegative matrix factorization. IEEE/ACM Trans Audio Speech Lang Process 26(9):1512–1527. https://doi.org/10.1109/TASLP.2018.2830105CrossRef

Défossez A, Bach F, Usunier N, Bottou L (2019) Music source separation in the waveform domain (2019)

Durrieu JL, Richard G, David B, Fevotte C (2010) Source/filter model for unsupervised main melody extraction from polyphonic audio signals. IEEE Trans Audio Speech Lang Process 18(3):564–575. https://doi.org/10.1109/TASL.2010.2041114CrossRef

Ewert S, Muller M (2011) Estimating note intensities in music recordings. In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 385–388. IEEE. https://doi.org/10.1109/ICASSP.2011.5946421

Févotte C, Bertin N, Durrieu JL (2009) Nonnegative matrix factorization with the Itakura-Saito divergence: with application to music analysis. Neural Comput 21(3):793–830. https://doi.org/10.1162/neco.2008.04-08-771CrossRefMATH

Herre J, Falch C, Mahne D, Del Galdo G, Kallinger M, Thiergart O (2010) Interactive teleconferencing combining spatial Audio Object Coding and DirAC technology. In: 128th Audio Engineering Society Convention 2010, vol. 3, pp. 1579–1590

Huang PS, Chen SD, Smaragdis P, Hasegawa-Johnson M (2012) Singing-Voice Separation From Monaural Recordings Using Robust Principal Component Analysis. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 57–60

10.

Ito N, Nakatani T (2019) FastMNMF: Joint Diagonalization Based Accelerated Algorithms for Multichannel Nonnegative Matrix Factorization. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. https://doi.org/10.1109/ICASSP.2019.8682291

11.

Itoyama K, Goto M, Komatani K, Ogata T, Okuno HG (2008) Instrument equalizer for query-by-example retrieval: improving sound source separation based on Integrated harmonic and Inharmonic Models. Ismir. https://doi.org/10.1136/bmj.324.7341.827CrossRef

12.

Jensen JR, Christensen MG, Jensen SH (2013) Nonlinear least squares methods for joint DOA and pitch estimation. IEEE Trans audio Speech Lang Process 21(5):923–933. https://doi.org/10.1109/TASL.2013.2239290CrossRef

13.

Kitamura D, Ono N, Sawada H, Kameoka H, Saruwatari H (2016) Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization. IEEE/ACM Trans Audio Speech Lang Process 24(9):1626–1641. https://doi.org/10.1109/TASLP.2016.2577880CrossRef

14.

Li B, Liu X, Dinesh K, Duan Z, Sharma G (2019) Creating a multitrack classical music performance dataset for multimodal music analysis: challenges, insights, and applications. IEEE Trans Multimedia 21(2):522–535. https://doi.org/10.1109/TMM.2018.2856090CrossRef

15.

Liutkus A, Durrieu JL, Daudet L, Richard G (2013) An overview of informed audio source separation. In: 2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), pp. 1–4. IEEE. https://doi.org/10.1109/WIAMIS.2013.6616139

16.

Marro C, Mahieux Y, Simmer K (1998) Analysis of noise reduction and dereverberation techniques based on microphone arrays with postfiltering. IEEE Trans Speech Audio Process 6(3):240–259. https://doi.org/10.1109/89.668818CrossRef

17.

McDonough J, Kumatani K (2012) Microphone Arrays. Techniques for Noise Robustness in Automatic Speech Recognition. Wiley, Chichester, UK, pp 109–157. https://doi.org/10.1002/9781118392683.ch6CrossRef

18.

Merimaa J, Pulkki V (2005) Spatial impulse response rendering I: analysis and synthesis. AES J Audio Eng Soc 53(12):1115–1127

19.

Mitsufuji Y, Roebel A (2013) Sound source separation based on non-negative tensor factorization incorporating spatial cue as prior knowledge. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 71–75. IEEE. https://doi.org/10.1109/ICASSP.2013.6637611

20.

Mitsufuji Y, Uhlich S, Takamune N, Kitamura D, Koyama S, Saruwatari H (2020) Multichannel non-negative matrix factorization using nanded spatial covariance matrices in wavenumber domain. IEEE/ACM Trans Audio Speech Lang Process 28:49–60. https://doi.org/10.1109/TASLP.2019.2948770CrossRef

21.

Munoz-Montoro AJ, Politis A, Drossos K, Carabias-Orti JJ (2020) Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CMNMF. In: 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6. IEEE. https://doi.org/10.1109/MMSP48831.2020.9287068

22.

Nikunen J, Virtanen T (2014) Direction of arrival based spatial covariance model for blind sound source separation. IEEE/ACM Trans Audio Speech Lang Process 22(3):727–739. https://doi.org/10.1109/TASLP.2014.2303576CrossRef

23.

Nikunen J, Virtanen T (2014) Multichannel audio separation by direction of arrival based spatial covariance model and non-negative matrix factorization. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 6677–6681. IEEE. https://doi.org/10.1109/ICASSP.2014.6854892

24.

Nugraha AA, Liutkus A, Vincent E (2016) Multichannel audio source separation with deep neural networks. IEEE/ACM Trans Audio Speech Lang Process 24(9):1652–1664. https://doi.org/10.1109/TASLP.2016.2580946CrossRef

25.

Pulkki V (2007) Spatial sound reproduction with directional audio coding. AES: J Audio Eng Soc 55(6):503–516

26.

Sawada H, Kameoka H, Araki S, Ueda N (2013) Multichannel extensions of non-negative matrix factorization with complex-valued data. IEEE Trans Audio Speech Lang Process 21(5):971–982. https://doi.org/10.1109/TASL.2013.2239990CrossRef

27.

Sekiguchi K, Bando Y, Nugraha AA, Yoshii K, Kawahara T (2020) Fast multichannel nonnegative matrix factorization with directivity-aware jointly-diagonalizable spatial covariance matrices for blind source separation. IEEE/ACM Trans Audio Speech Lang Process 28:2610–2625. https://doi.org/10.1109/TASLP.2020.3019181CrossRef

28.

Sekiguchi K, Nugraha AA, Bando Y, Yoshii K (2019) Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5. IEEE. https://doi.org/10.23919/EUSIPCO.2019.8902557

29.

Smaragdis P (2012) Extraction of Speech from mixture signals. Techniques for noise robustness in automatic speech recognition. Wiley, Chichester, UK, pp 87–108. https://doi.org/10.1002/9781118392683.ch5CrossRef

30.

Tashev IJ (2009) Sound capture and processing. Wiley, Chichester, UK. https://doi.org/10.1002/9780470994443CrossRef

31.

Vincent E, Gribonval R, Fevotte C (2006) Performance measurement in blind audio source separation. IEEE Trans Audio Speech Lang Process 14(4):1462–1469. https://doi.org/10.1109/TSA.2005.858005CrossRef

32.

Wang L, Ding H, Yin F (2010) Combining superdirective beamforming and frequency-domain blind source separation for highly reverberant signals. EURASIP J Audio Speech Process 2010(1):1–13. https://doi.org/10.1155/2010/797962CrossRef

Titel: Parallel multichannel blind source separation using a spatial covariance model and nonnegative matrix factorization
verfasst von: A. J. Muñoz-Montoro
J. J. Carabias-Orti
R. Cortina
S. García-Galán
J. Ranilla
Publikationsdatum: 06.04.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 10/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03771-y

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