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Experimental analysis of optimal window length for independent low-rank matrix analysis

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Experimental analysis of optimal window length for independent low-rank matrix analysis

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Daichi Kitamura, Nobutaka Ono, and Hiroshi Saruwatari, "Experimental analysis of optimal window length for independent low-rank matrix analysis," Proceedings of The 2017 European Signal Processing Conference (EUSIPCO 2017), pp. 1210–1214, Kos, Greece, August 2017 (Invited Special Session).
Presented at 25th European Signal Processing Conference (EUSIPCO) 2017, "SS14: Multivariate Analysis for Audio Signal Source Enhancement," 14:30-16:10, August 30, 2017.

Daichi Kitamura, Nobutaka Ono, and Hiroshi Saruwatari, "Experimental analysis of optimal window length for independent low-rank matrix analysis," Proceedings of The 2017 European Signal Processing Conference (EUSIPCO 2017), pp. 1210–1214, Kos, Greece, August 2017 (Invited Special Session).
Presented at 25th European Signal Processing Conference (EUSIPCO) 2017, "SS14: Multivariate Analysis for Audio Signal Source Enhancement," 14:30-16:10, August 30, 2017.

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Experimental analysis of optimal window length for independent low-rank matrix analysis

  1. 1. Experimental analysis of optimal window length for independent low-rank matrix analysis Daichi Kitamura Nobutaka Ono Hiroshi Saruwatari 25th European Signal Processing Conference (EUSIPCO) 2017 SS14: Multivariate Analysis for Audio Signal Source Enhancement August 30, 14:30-16:10 The University of Tokyo, Japan National Institute of Informatics, Japan The University of Tokyo, Japan
  2. 2. Contents • Background – Blind source separation (BSS) for audio signals – Motivation: fundamental limitation in frequency-domain BSS • Methods – Frequency-domain independent component analysis (FDICA) – Independent vector analysis (IVA) – Independent low-rank matrix analysis (ILRMA) • Experimental analysis – Optimal window length • Music signals and speech signals • Ideal case and more practical case • Conclusion 2
  3. 3. Contents • Background – Blind source separation (BSS) for audio signals – Motivation: fundamental limitation in frequency-domain BSS • Methods – Frequency-domain independent component analysis (FDICA) – Independent vector analysis (IVA) – Independent low-rank matrix analysis (ILRMA) • Experimental analysis – Optimal window length • Music signals and speech signals • Ideal case and more practical case • Conclusion 3
  4. 4. • Blind source separation (BSS) for audio signals – separates original audio sources – does not require prior information of recording conditions • locations of mics and sources, room geometry, timbres, etc. – can be available for many audio app. • Consider only “determined” situation Background 4 Recording mixture Separated guitar BSS Sources Observed Estimated Mixing system Demixing system # of mics # of sources
  5. 5. • Basic theories and their evolution History of BSS for audio signals 5 1994 1998 2013 1999 2012 Age Many permutation solvers for FDICA Apply NMF to many tasks Generative models in NMF Many extensions of NMF Independent component analysis (ICA) Nonnegative matrix factorization (NMF) Frequency-domain ICA (FDICA) Itakura–Saito NMF (ISNMF) Independent vector analysis (IVA) Multichannel NMF Independent low-rank matrix analysis (ILRMA) *Depicting only popular methods 2016 2009 2006 2011 Auxiliary-function-based IVA (AuxIVA) Time-varying Gaussian IVA
  6. 6. • Basic theories and their evolution History of BSS for audio signals 6 1994 1998 2013 1999 2012 Age Many permutation solvers for FDICA Apply NMF to many tasks Generative models in NMF Many extensions of NMF Independent component analysis (ICA) Nonnegative matrix factorization (NMF) Frequency-domain ICA (FDICA) Itakura–Saito NMF (ISNMF) Independent vector analysis (IVA) Multichannel NMF Independent low-rank matrix analysis (ILRMA) *Depicting only popular methods 2016 2009 2006 2011 Auxiliary-function-based IVA (AuxIVA) Time-varying Gaussian IVA
  7. 7. Motivation: fundamental limitation of BSS • Mixing assumption in frequency-domain BSS – “Linear time-invariant mixture” or “rank-1 spatial model” – Valid only when • Too long window also causes another problem – Number of time frames (samples) decreases • Trade-off between short and long window [S. Araki+, 2003] – FDICA suffers from the trade-off – What about for BSS methods with structural source model? • IVA and ILRMA 7 : frequency binsObserved multichannel signal Source signalsFrequency-wise mixing matrix : time frames Statistical bias will increase and estimation becomes unstable window length used in STFT length of room reverberation Performance Window length Optimal length
  8. 8. Contents • Background – Blind source separation (BSS) for audio signals – Motivation: fundamental limitation in frequency-domain BSS • Methods – Frequency-domain independent component analysis (FDICA) – Independent vector analysis (IVA) – Independent low-rank matrix analysis (ILRMA) • Experimental analysis – Optimal window length • Music signals and speech signals • Ideal case and more practical case • Conclusion 8
  9. 9. • Frequency-domain ICA (FDICA) [P. Smaragdis, 1998] • Independent vector analysis (IVA) [A. Hiroe, 2006], [T. Kim, 2006] BSS methods: FDICA and IVA 9 Observed Update separation filter so that the estimated signals obey non-Gaussian distribution we assumed Estimated Demixing matrix Current empirical dist. Non-Gaussian source dist. STFT Frequency Time Frequency Time Observed Estimated Current empirical dist. STFT Frequency Time Frequency Time Non-Gaussian spherical source dist. Scalar r.v.s Vector (multivariate) r.v.s Update separation filter so that the estimated signals obey non-Gaussian distribution we assumed Mixture is close to Gaussian signal because of CLT Source obeys non- Gaussian dist. Mutually independent Demixing matrix Mutually independent
  10. 10. • Spherical Laplace distribution in IVA • Zero-mean complex Gaussian distribution with TF- varying variance (Itakura-Saito NMF)[C. Févotte+, 2009] 10 Frequency-uniform scale Extension of source distribution in IVA Zero-mean complex Gaussian in each TF bin Low-rank decomposition with NMF Spherical Laplace (bivariate) Frequency vector (I-dimensional) Time-frequency-varying variance Time-frequency matrix (IJ-dimensional) Extended to a more flexible model
  11. 11. • Power spectrogram corresponds to variances in TF plane Generative source model in ISNMF 11 Frequencybin Time frame : Power spectrogram Small value of power Large value of power Complex Gaussian distribution with TF-varying variance If we marginalize in terms of time or frequency, the distribution becomes non-Gaussian even though each TF grid is defined in Gaussian distribution Grayscale shows the value of variance
  12. 12. BSS methods: ILRMA • Independent low-rank matrix analysis (ILRMA) [D. Kitamura+,2016] – Unification of IVA and ISNMF – Source model in ILRMA 12 Frequency Basis Basis Time Number of bases can be set to arbitrary value Frequency Time Observed Estimated Low-rank decomposition Time Frequency Frequency Time Update demixing matrix so that estimated signals have low-rank structure in time-frequency domain STFT Demixing matrix
  13. 13. Comparison of source models 13 FDICA source model Non-Gaussian scalar variable IVA source model Non-Gaussian vector variable with higher-order correlation ILRMA source model Non-Gaussian matrix variable with low-rank time-frequency structure Rank of TF matrix of mixture Rank of TF matrix of each source
  14. 14. Contents • Background – Blind source separation (BSS) for audio signals – Motivation: fundamental limitation in frequency-domain BSS • Methods – Frequency-domain independent component analysis (FDICA) – Independent vector analysis (IVA) – Independent low-rank matrix analysis (ILRMA) • Experimental analysis – Optimal window length • Music signals and speech signals • Ideal case and more practical case • Conclusion 14
  15. 15. Experimental analysis • Window length in STFT – If window length is too short • Mixing assumption does not hold anymore – If window length is too long • Estimation becomes unstable (# of time frames decreases) 15 Frequency Time … DFT DFT DFT Spectrogram … Window length (= DFT length) Shift length Window function Waveform • Our expectation – Full time-frequency modeling of sources in ILRMA may improve the robustness to a decrease in the number of time frames
  16. 16. Experimental analysis • Dataset: 4 music and 4 speech from SiSEC [S. Araki+, 2012] • Mixing: convolution with RIR in RWCP [S. Nakamura+, 2000] 16 Signal Data name Source (1/2) Length [s] Music bearlin-roads acoustic_guit_main/vocals 14.6 Music another_dreamer-the_ones_we_love guitar/vocals 25.6 Music fort_minor-remember_the_name violins_synth/vocals 24.6 Music ultimate_nz_tour guitar/synth 18.6 Speech dev1_female4 src_1/src_2 10.0 Speech dev1_female4 src_3/src_4 10.0 Speech dev1_male4 src_1/src_2 10.0 Speech dev1_male4 src_3/src_4 10.0 2 m Source 1 5.66cm 50 50 2 m 5.66cm 60 60 Impulse response E2A (reverberation time: T60 = 300 ms) Impulse response JR2 (reverberation time: T60 = 470 ms) Source 2 Source 1 Source 2
  17. 17. Experimental analysis • Compared methods – FDICA+IPS (ideal permutation solver) • Align permutation of estimated components using the reference (oracle) source spectrogram (upper limit performance of FDICA) – FDICA+DOA (DOA-based permutation solver) [S. Kurita+, 2000] • Align permutation of estimated components using DOA after FDICA – IVA [N. Ono, 2011] • using auxiliary function method (a.k.a. MM algorithm) in optimization – ILRMA [D. Kitamura+, 2016] • with several numbers of bases • Other conditions – Window function: Hamming window – Window length: 32 ~ 2048 ms – Shift length: Always quarter of window length 17
  18. 18. Comparison using ideal initialization: condition • Set initial value of demixing matrix to oracle: – This initial value provides the best separation performance under the assumption • Set initial value of source model as oracle (only for ILRMA): 18 Power spectrogram of th source FDICA+DOA & IVA: spatial oracle initialization FDICA+IPS & ILRMA: spatial and spectral oracle initialization
  19. 19. Comparison using ideal initialization: results 19 Music T60 =0.30 s Music T60 =0.47 s Speech T60 =0.30 s Speech T60 =0.47 s
  20. 20. Comparison using random initialization: condition • Set initial value of demixing matrix to identity matrix • Set initial value of source model to uniform random value between [0,1] (only for ILRMA) 20 FDICA+DOA, IVA, & ILRMA: fully blind method FDICA+IPS: using oracle spectrogram
  21. 21. Comparison using random initialization: results 21 Music T60 =0.30 s Music T60 =0.47 s Speech T60 =0.30 s Speech T60 =0.47 s
  22. 22. Conclusion • In the case of ILRMA with oracle initialization, the robustness to long windows (fewer time frames) can be improved – optimal window length is longer than that in FDICA or IVA – thanks to employing not only the independence between sources but also a full modeling of time-frequency structure for the estimation of the demixing matrix • In a practical situation (fully blind case), – optimal window length is similar to that in FDICA or IVA – difficulty of the blind estimation of a precise spectral model in ILRMA 22 Thank you for your attention!

Editor's Notes

  • This talk treats blind source separation problem, BSS, which is a separation technique of individual sources from the recorded mixture.
    The word “blind” means that the method does not require any prior information about the recording conditions, such as locations of microphones, sources, and room geometry.
    This kind of technique is very useful for many audio applications as a front-end system.
    In this talk, we only consider a “determined” situation, namely, the number of microphones is always equal to the number of sources.
  • This slid shows a history of basic theories in audio BSS.
    For acoustic signals, independent component analysis, ICA, was applied to the frequency domain signals as FDICA. After that, many permutation solvers for FDICA have been proposed, but eventually, an elegant solution, independent vector analysis, IVA was proposed. It is still extended to more flexible models.
    On the other hand, nonnegative matrix factorization, NMF, is also developed and extended to a multichannel signals for source separation problems.
    Recently, we have developed a new framework, which unifies these two powerful theories, called independent low-rank matrix analysis, ILRMA.
    I will explain about the detail, but in this talk,
  • we only focus on only these algorithms, FDICA, IVA, and ILRMA.
  • I here explain the motivation of this talk.
    In many frequency-domain BSS techniques, this equation, x=As, is always assumed, where x is a multichannel mixture signal in the frequency domain, i is a frequency bin and j is a time frames, A is a frequency-wise mixing matrix, and s is an original source.
    This is often called “linear time-invariant mixture” or a “rank-1 spatial model,” and this assumption is valid only when the window length in STFT is much longer than the length of room reverberation. So, we must use a longer window in STFT for validating this mixing assumption.
    However, if we use too much long window in STFT, the statistical bias will increase and the estimation becomes unstable. This is because the number of time frames J decreases.
    Therefore, there is a trade-off between short and long window lengths like this figure. In the paper in 2003, this trade-off was revealed only for FDICA. But we don’t know about this issue for the BSS methods that employ s structural source model, I mean it’s an IVA or ILRMA.
    So, in this talk, we experimentally confirm about this point, about the optimal window length for the new BSS techniques including ILRMA.
  • I briefly explain the separation mechanism in FDICA and IVA.
    In FDICA, ICA is applied to each frequency bin considering the scalar time-series signal as a random variable, and we maximize its non-Gaussianity to estimate the frequency-wise demixing matrix.
    In IVA, we consider a vector time-series random variable including all frequencies like this figure, then we assume a multivariate non-Gaussian distribution with a spherical property. Since spherical property ensures higher-order correlation among frequency bins, the permutation problem can be avoided in IVA.
  • The spherical source distribution in IVA can be extended to a more flexible model. We have extend it to a local Gaussian model, which employs a zero-mean complex Gaussian distribution with time-frequency-varying variance.
    Namely, in each time-frequency slot, i and j, complex Gaussian distribution is defined, and its variance, r, can fluctuate depending on time and frequency.
    This generative model is equivalent to that in Itakura-Saito NMF, and the variance r can be decomposed into a basis matrix T and an activation matrix V.
  • This is a graphical interpretation of the source model in ISNMF.
    In each time-frequency slot, zero-mean complex Gaussian distribution is defined, and they are mutually independent in all time, frequency, and sources.
    Now, the variance of these Gaussians is corresponding to the power spectrogram.
    Therefore, in the slot that has a strong power, such as a spectral peak, the Gaussian becomes wider, and the large power component can easily be generated.
    Note that, even though each slot is Gaussian, the marginal distribution in terms of time is non-Gaussian, because the variance fluctuates.
    So, since this matrix generative model is non-Gaussian, we can use this distribution as a source model in ICA-based method
  • resulting in an independent low-rank matrix analysis (ILRMA). Therefore, ILRMA is a unified method of IVA and ISNMF, and we employed NMF source model to capture the low-rank time-frequency structures of each source.
    This source model can improve the estimation accuracy of the demixing matrix.
  • This is a comparison of source models in FDICA, IVA, and ILRMA again.
    The important idea used in ILRMA is that the rank of TF matrix of mixture signal is always grater than the rank of TF matrix of each source before mixing.
    So, if we assume not only the independence between source but also a low-rank TF structure for each source, the separation will be done accurately.
  • As I already explained, the window length in STFT affects the performance of ICA-based separation.
    If we use too short window, the mixing assumption, x=As, does not hold anymore, and if we use too long window, the estimation becomes unstable because the number of time frames J decreases.
    However, ILRMA employs a full time-frequency modeling of sources. This model may improve the robustness to a decrease in J. This is our expectation.
    Let’s check about this issue by the experiment.
  • Here we used 4 music and 4 speech signals obtained from SiSEC database, and we produced the observed signal by convoluting the impulse response shown in the bottom.
    We used two types of impulse response, one has 300-ms-long reverberation, and the other one is 470 ms.
  • We compared 4 methods, FDICA + ideal permutation solver, FDICA + DOA-based permutation solver, IVA, and ILRMA.
    In FDICA+IPS, we used the reference, oracle source spectrogram. So this is an upper limit of FDICA.
    FDICA+DOA is a blind method that uses DOA clustering for solving the permutation problem.
    Of cause IVA and ILRMA are also blind method.
    Then, we used Hamming window with various window lengths.
  • First, we show the results with ideal initialization case. Namely, we first give a correct answer of demixing matrix as an initial value, which can be calculated using the oracle source s. So, the initial value provides the best separation performance here.
    In addition, only for ILRMA, we set the initial value of NMF model T and V as the oracle values.
    Therefore, FDICA+DOA and IVA are using the spatial oracle initialization, and FDICA+IPS and ILRMA are using spatial and spectral oracle initialization.
  • This is the result. The left ones are music, and right ones are the speech, and the reverberation time is short (top) and long (bottom).
    The horizontal axis shows the window length, and the vertical axis shows the separation performance.
    The colored lines are the results of ILRMA with various numbers of NMF bases.
    In the music results, we can see that FDICA and IVA could not achieve the good separation when the window becomes long.
    In ILRMA, the performance maintains even in a long long windows. This is obtained from the full modeling of time-frequency structure of each source.
    However, for the speech signals, the performance of ILRMA becomes worse. We guess this is because speech does not have a low-rank time-frequency structures, and the source model could not capture the precise speech structures even if we set the source model as an oracle one.
  • Next, we show the results with fully blind situation. Initial W is set to identity matrix, and the initial source model is randomized.
    Note that FDICA+IPS still uses the oracle spectrogram for solving the permutation.
  • This is the result. We could not obtain the same results as the previous case with ideal initialization.
    The performance of all the methods is degraded when the window length becomes long.
    Therefore, at least we can say that, ILRMA has a good potential to separate the sources even in a long window case, but in practice, the blind estimation of precise source model is a difficult problem.
  • This figure shows the difference of source models in IVA and ILRMA.
    Since IVA assumes frequency-uniform scale, it is almost an NMF with only one flat basis.
    On the other hand, ILRMA has more flexible source model with arbitrary number of spectral bases. So we can capture more precise TF structure of each source.
  • 提案手法ILRMAの対数尤度関数はこのように得られます.ここで(クリック)青丸で囲った空間分離フィルタWと,赤丸で囲ったNMF音源モデルTVが求めるべき変数になります.(クリック)
    さらにこの式は,(クリック)前半が従来のIVAのコスト関数と等価であり,(クリック)後半が従来のNMFのコスト関数と等価です.(クリック)
    したがって,IVAとNMFの反復更新式を交互に反復することで全変数を容易に推定できます.
    さらに,音源毎に適切なランク数を潜在変数で適応的に決定することも可能です.
    これは,冒頭で示した通り,音楽信号といえどもボーカルはあまり低ランクにならず,ドラム信号は低ランク,といったことが起こりえますので,音源毎の適切なランクが変わります.
    そのような状況に対して尤度最大化の基準で自動的に基底を割り振るのがこの潜在変数の役割です.
  • ILRMAの反復更新式はこのように導出できます.
    空間分離フィルタの更新と音源モデルの更新を交互に行うことで,全変数が最適化されます.
    これらの反復計算で尤度が単調増加することが保証されているので,初期値近傍の局所解への収束が保証されています.
  • つまり,提案手法はまず空間分離フィルタを学習し,それで分離された信号の音色構造をNMFで学習,その結果得られる音源モデルを空間分離フィルタの学習に再利用し,さらに高精度な分離信号が得られる,という反復になります.
    このプロセスを何度も更新することで,音源毎の明確な音色構造が捉えられ,空間分離フィルタの性能向上が期待できます.
  • また,論文ではNMFの多チャネル信号への拡張手法である多チャネルNMFとILRMAが密接に関連しているという事実を明らかにしています.
    簡単に説明いたしますと,従来の多チャネルNMFで定義されている空間情報に関するモデル「空間相関行列」のランクが1となる制約を課した場合とILRMAが等価となる,という事実です.
    ただし,多チャネルNMFは混合系を推定する手法であり,ILRMAやIVAのように分離系を推定する技術とは異なります.そのため,多チャネルNMFは計算効率や不安定性の観点から実用性にやや欠ける点があります.これに関しては比較実験で示します.
  • さて,IVAの音源モデルと提案手法の音源モデルの違いについて説明します.
    IVAは,球対称な多次元分布に基づくため,周波数方向に一様な分散を持つ音源モデルになります.これは1本の基底のNMFに非常に近いと言えます.
    一方提案手法は,任意の数の基底を用いたNMFが音源モデルになります.従って,この図のように音楽信号の具体的な調波構造を捉えることができます.
    このように明確な音源モデルを推定することで,独立性基準での分離性能の向上が期待できます.
  • we only focus on these three algorithms, FDICA, IVA, and ILRMA.

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