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1 pp c * 1, 2 1 2, BM CSS CSS CSM BM CSM CSS CSS CSM Blind source separation, Sparseness, Binary mas, Musical noise, Cepstral smoothing, Separated speech signals 1. BSS [1] BSS BSS ICA [2] e.g, [3] N M BSS Cepstral smoothing of separated signals for underdetermined speech separation, by Yumi Ansai, Shoo Arai, Shoji Maino, Tomohiro Naatani, Taeshi Yamada, Atsushi Naamura and Nobuhio Kitawai. 1 NTT BM [3 15] BM BSS BM [16] BM CSM: Cepstral Smoothing of spectral Mass CSM CSM BM CSM
2 75 [16] BM BM CSS: Cepstral Smoothing of separated Signals CSM [17] CSM CSS CSS 2 BM BSS 3 BM CSM 4 CSS 5 CSS CSM s i (i =1,...,N) M j x j (j =1,...,M) N L x j (n) = h ji (l)s i (n l +1) i=1 l=1 (j =1,...,M) (1) h ji i j l n 1 BSS x j y i N >M N M [7, 18 21] 1 BSS N >M N X j (f,m) = H ji (f)s i (f,m) i=1 (j =1,...,M) (2) H ji (f) i j S i (f,m) X j (f,m) STFT f m 2.2 BM [3, 6] 2 N -means X(f,m) =[X 1 (f,m),...,x M (f,m)] T [6] { 1 X(f,m) C, M (f,m) = (3) otherwise M min C M min 0 (> 0) BM Y (f,m) =M (f,m)x j (f,m) (4) Y (f,m) STFT y BM [15] CSM BM CSM
3 l pitch =argmax{m cepst (l, m) l low l l high } l (8) {l low,l high } Hz [16] (8) 2 BM CSM [16] 2 CSM M cepst (l, m) = DFT 1 {ln(m (f,m)) f=0,...,f 1 } (5) l DFT{ } F M (f,m) (3) M min =0.01 M cepst M cepst (l, m) =β l M cepst (l, m 1) +(1 β l )M cepst (l, m) (6) (6) l β l β env if l {0,...,l env } β l = β pitch if l = l pitch β pea if l {(l env +1),...,F/2}\{l pitch } (7) F (5) F [16] l {0,...,l env } M cepst (l, m) M (f,m) β env M (f,m) l = l pitch β pitch M (f,m) β pea (>β pitch ) m l pitch l pitch l pitch (7) l>f/2 DFT M cepst (l, m) DFT M (f,m) M (f,m) =exp(dft{m cepst (l, m) l=0,...,f 1 }) (9) Y (f,m) =M (f,m)x j (f,m) (10) 3.2 CSM 3 BM CSM M =3 N =4 BM CSM 4 BM 3(A) CSM 3(B) CSM CSM BM BM 1 M min 2 4 (A) BM (B) (C)(D) M cepst 4(C) 4(A) BM 4(B) (7)
4 劣決定音源分離のための分離音声のケプストラムスムージング 図 3 分離信号のスペクトログラム 77
5 CSM (A) (B) BM (C) CSM (D) CSM CSS 3.2 CSM CSS
6 79 1 [16] f s =8Hz l env =16 β env =0 F = 512 l low =32 β pitch =0.4 M min =0.01 l high = 228 β pea =0.8 5 CSS 4.2 β pea 5 CSS 2 CSS CSM 4.2 CSS (4) Y (f,m) Y cepst (l, m)=dft 1 {ln(y (f,m)) f=0,...,f 1 } (11) Y cepst Y cepst (l, m) =β l Y cepst (l, m 1) +(1 β l )Y cepst (l, m) (12) β l (7) l {(l env + 1),...,F/2}\{l pitch } (12) β pea l pitch l pitch =argmax{y cepst (l, m) l low l l high } l (13) 2 β l original case 1 case 2 case 3 CSM β pitch β pea CSS β pitch β pea [16] l pitch l > F/2 DFT Y cepst (l, m) Y cepst (l, m) (9) DFT STFT y CSS CSM 1 CSS CSM CSM β l [16] 1 CSS β l 3 β l 2 β env 0 β pitch β pea CSS CSM [16] CSM 2 BM CSM CSS AO BM X 1 (f,m) BM [22] MRI BM M (f,m) 4 M (f 1,m), M (f +1,m), M (f,m 1), M (f,m +1)) 0
7 musical noise 4 musical noise 3 musical noise 2 musical noise 1 musical noise Sources to Artifacts Ratio (SAR): 6 M (f,m) 0 M (f,m) 4 1 M (f,m) 1 MRI 2 Y (f,m) Y (f,m) = 1 2 Y (f,m) {Y (f 1,m)+Y (f +1,m) +Y (f,m 1) + Y (f,m +1)} (14) [23] Perceptual SS BM [23] ms M =3 N = f s DFT F 1 F/2 [24] Signal to Distortion Ratio (SDR): Source Image to Spatial distortion Ratio (ISR): Source to Interference Ratio (SIR): SDR ISR SIR SAR [24] SIR ISR SAR db 11 Mean Opinion Score (MOS) BM CSS 3 BM, CSM, CSS (a) CSM CSS MOS BM CSM CSS SIR BM ISR SAR BM CSM original [16] CSS β l CSS case2 CSS ISR SAR CSM original CSM CSS CSS MOS CSM 8 case2 β l CSS 3(A)
8 81 劣決定音源分離のための分離音声のケプストラムスムージング 図 7(b) は ケプストラムスムージング手法 5.1 節 減に効果的であることが分かる と同一の CSM や CSS と 5.2 節で述べたミュージ このように ケプストラムスムージング手法 CSM カルノイズ低減手法との比較結果を示している CSM や CSS はミュージカルノイズ低減に効果的である や CSS の ISR と SAR は他手法より低いが 一方で また 図 7(a) より CSS の性能とパラメータ βpea ミュージカルノイズの量に着目した MOS 値は CSM との関係を読み取ることができる すなわち βpea が や CSS の方が高く 他手法よりミュージカルノイズ低 大きな値の場合 case1 には MOS 値が高く ISR や SAR が低い すなわちミュージカルノイズが低減され 信号歪は大きい結果が得られる 一方 βpea が小さな 値の場合 case2, case3 には信号歪は小さいものの ミュージカルノイズが顕著となる これは 4.2 節に述 べたミュージカルノイズの軽減の程度とスペクトル微 細構造の保持の程度のトレードオフを示している 5.5 考 察 前節で述べたとおり 提案法である CSS は CSM よ り高い ISR や SAR を持つことが分かった また CSS 及び CSM 法はミュージカルノイズ低減に効果的であ ることも示された しかし上述したとおり CSS 及び CSM 法は BM 法 図 7(a) や 5.2 節に述べたミュージカルノイズ低減手 法 図 7(b) と比較して ISR や SAR が低くなること から ケプストラムスムージング手法ではミュージカ ルノイズとは異なる歪が生じることが分かった 実際 図 7 各歪値と MOS の比較結果 図 8 に著者らが聴取したところでは ケプストラムスムージ CSS による分離信号のスペクトログラム
9 (A) (B) BM (C) CSM (D) CSS ISR SAR CSS case2 CSM original ISR SAR MOS CSS case2 CSM 9 BM CSM CSS
10 83 10 CSM bin BM 9 BM BM CSM 9(B) (C) CSM 0 l env CSM 10 BM 1 M min 2 10 (4) BM CSM 10 CSM (10) 3.2 CSM CSM CSS BM CSS 9 (B) (D) CSS BM (A) CSS CSM CSS CSS CSM CSS 6. BSS CSM CSS CSS CSS CSS CSM CSS NTT [ 1 ] S. Hayin, Ed., Unsupervised Adaptive Filtering, Volume I: Blind Source Separation (Wiley, New Yor, 2000). [ 2 ] A. Hyvärinen, J. Karhunen and E. Oja, Independent Component Analysis (John Wiley & Sons, New Yor, 2001). [3] Ö. Yilmaz and S. Richard, Blind separation of speech mixtures via time-frequency masing, IEEE Trans. Signal Process., 52, (2004). [ 4 ] N. Roman and D. Wang, Binaural sound segregation for multisource reverberant environments, Proc. ICASSP 2004, Vol. II, pp (2004).
11 [ 5 ] S. Ricard and Ö. Yilmaz, On the W-disjoint orthogonality of speech, Proc. ICASSP 2002, Vol. 1, pp (2002). [ 6 ] S. Arai, H. Sawada, R. Muai and S. Maino, Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors, Signal Process., 77, (2007). [ 7 ] P. Bofill and M. Zibulevsy, Blind separation of more sources than mixtures using sparsity of their short-time Fourier transform, Proc. ICA 2000, pp (2000). [ 8 ] A. Jourjine, S. Ricard and Ö. Yilmaz, Blind separation of disjoint orthogonal signals: Demixing N sources from 2 mixtures, Proc. ICASSP 2000, Vol. 5, pp (2000). [ 9 ] M. Aoi, M. Oamoto, S. Aoi, H. Matsui, T. Saurai and Y. Kaneda, Sound source segregation based on estimating incident angle of each frequency component of input signals acquired by multiple microphones, Acoust. Sci. & Tech., 22, (2001). [10] S. Ricard, R. Balan and J. Rosca, Real-time time-frequency based blind source separation, Proc. ICA 2001, pp (2001). [11] N. Roman, D. Wang and G.J. Brown, Speech segregation based on sound localization, J. Acoust. Soc. Am., 114, (2003). [12] S. Arai, S. Maino, A. Blin, R. Muai and H. Sawada, Blind separation of more speech than sensors with less distortion by combining sparseness and ICA, Proc. IWAENC 2003, pp (2003). [13] J.M. Peterson and S. Kadambe, A probabilistic approach for blind source separation of underdetermined convolutive mixtures, Proc. ICASSP 2003, Vol. VI, pp (2003). [14] S. Arai, S. Maino, A. Blin, R. Muai and H. Sawada, Underdetermined blind separation for speech in real environments with sparseness and ICA, Proc. ICASSP 2004, Vol. III, pp (2004). [15] S. Arai, H. Sawada, R. Muai and S. Maino, Blind sparse source separation with spatially smoothed time-frequency masing, Proc. IWAENC 2006 (2006). [16] N. Madhu, C. Breithaupt and R. Martin, Temporal smoothing of spectral mass in the cepstral domain for speech separation, Proc. ICASSP 2008, pp (2008). [17] Y. Ansai, S. Arai, S. Maino, T. Naatani, T. Yamada, A. Naamura and N. Kitawai, Cepstral smoothing of separated signals for underdetermined speech separation, Proc. ISCAS 2010, pp (2010). [18] P. Bofill and M. Zibulevsy, Blind separataion of more sources than mixtures using sparsity of their short-time-fourier transform, Proc. ICA 2000, pp (2000). [19] A. Blin, S. Arai and S. Maino, Blind source separation when speech signals outnumber sensors using an sparseness mixing matrix combination, Proc. IWAENC 2003, pp (2003). [20] Y. Izumi, N. Ono and S. Sagayama, Sparsenessbased 2ch BSS using EM algorithm in reverberant environment, Proc. WASPAA, pp (2007). [21],,, BSS,, pp (2008). [22],,,, pp (2004.3). [23] N. Virag, Single channel speech enhancement based on masing properties of the human auditory system, IEEE Trans. Speech Audio Process., 7, (1999). [24] E. Vincent, H. Sawada, P. Bofill, S. Maino and J.P. Rosca, First stereo audio source separation evaluation campaign: Data, algorithms and results, Proc. ICA 2007, pp (2007) IEEE 56 NTT IEEE Distinguished Lecturer IEEE Fellow Fellow 03 IEEE 11 IEEE
12 ATR 12 / IEEE IEEE Fellow
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