sigmus201007_fujihara.dvi
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1 1 1 1) W-PST W-PST W-PST W-PST Singing voice conversion method by using spectral envelope of singing voice estimated from polyphonic music Hiromasa Fujihara 1 and Masataka Goto 1 This paper describes a singing voice conversion method that can deal with singing voices in polyphonic music. Conventional voice conversion methods only deal with monophonic singing voices. In this paper, we utilize the W-PST method proposed by Fujihara et al. 1), which can identify the frequency components of a singing voice in a polyphonic spectrum. We first describe our method of converting the vocal timbres of singing voices to those of other singers by maniplulating only frequency components of singing voices identified by the W-PST method. Since the W-PST method requires spectral envelopes of the singing voices, we then describe a method of estimating them from polyphonic music. We applied our method to actual musical audio signals and confirmed that it was able to convert the vocal timbre of the singing voices in polyphonic music. 1. F0 2) Drumix 3) Instrument Equalizer 4) Web Bonada 5) YAMAHA VOCALOID 6) HMM 7) Sinsy 8) 9) 11) STRAIGHT 12) 2 1 National Institute of Advanced Industrial Science and Technology (AIST) 1 c 2010 Information Processing Society of Japan
2 î 4*3 ¾ f 0 ' ¾ Log-Power Log-Power (a) d d Ì (b) d d 1 g v Y v, f g n Log-Power Y n, f d d d d Ì d d d d š (c) d d Y f Log-Power y( f ) (d) š 1) g v g n SIR STRAIGHT 13) 1) W- PST W-PST F0 W-PST Log-Power Y ' v, f (a) d d 2 + Log-Power ( f ; f0) (b) ¾ H Log-Power Y v, f (c) d d 1) 2. W-PST 1) W-PST W-PST CWT F0 W-PST SIR Signal-to- Interference Ratio F0 F0 F0 1) 2.1 W-PST 1) 1 (c) (d) 1(a) 1(b) 2 c 2010 Information Processing Society of Japan
3 変換元音響信号 ( 混合音 ) (a) 連続ウェーブレット変換 (g) スペクトルを操作 変換元観測スペクトル (d) SIR の推定 合成されたテンプレート 返還後のスペクトル (h) 逆連続ウェーブレット変換 (c) 変換元歌手のテンプレートを推定 変換元歌声テンプレート (e) 歌声テンプレートを変換先歌手のものと置き換える 返還後のスペクトルテンプレート 3 出力音響信号 変換先音響信号 ( 単独歌唱 ) 変換先歌手の歌声テンプレート (b) 連続ウェーブレット変換 変換先観測スペクトル (f) 変換先歌手のテンプレートを推定 2 SIR 2 (a) 2(b) 3 W-PST CD CWT 3(a),(b) W-PST 3(c) 3.3 SIR W-PST 3(d) 3(e) 1) 3(f) 3(g) ICWT 3(h) CWT CWT x(t) 3 c 2010 Information Processing Society of Japan
4 W (b, a) = 1 a Ψ(t) = x(t)ψ ( ) t b dt (1) a ( ) 1 exp t2 exp (iω 2πσ 2 0t) (2) 2σ 2 Ψ( ) Ψ( ) σ [ms] ω 0 1 b W (b, a) b a 2πa w 0 Hz (1) Ψ(t) 3σ <t<3σ (1) a (FFT) 10ms b b b y(f) a f y(f) =log( W (b, a) ) (3) f =log 2πa w 0 (4) y(f) Y f Y f Y f 2 Y v,f Y n,f Y f = log(exp(y v,f + g v)+exp(y n,f + g n)) (5) Y v,f Y n,f g v g n SIR (5) Y v,f Y n,f Y v,f N(μ v,f,σv,f) 2 (6) Y n,f N(μ n,f,σn,f 2 ) (7) N (μ, σ 2 ) μ σ 2 Y v,f 2 3 Y v,f = Y v,f + H(f; f 0) (8) N(μ v,f + H(f; f 0),σv,f) 2 (9) ( ) H(f; f 0)=log exp( (log f 0 +logh log f) 2 /2θH) 2 (10) h Y v,f N(μ v,f,σv,f) 2 H(f; f 0) F0 f 0 2(b) H(f; f 0) F 0 μ v,f σv,f μ 2 n,f σn,f 2 θh 2 15 cent Y f Y f = log(exp(y v,f + H(f; f 0)+g v)+exp(y n,f + g n)) (11) Y f (g v,g n) Y f p f (y; g v,g n) (11) Y f l(x 1,x 2)=log(exp(x 1)+ exp(x 2)) (x 1,x 2)=(μ v,f + H(f; f 0)+g v,μ n,f + g n) 1 l(x 1,x 2) exp (μ v,f + H(f; f 0)+g v) exp (μ v,f + H(f; f0)+gv)+exp(μ n,f + g n) x1 exp (μ n,f + g n) + exp (μ v,f + H(f; f0)+gv)+exp(μ x2 + C (12) n,f + g n) C x 1 x 2 g v g n (12) x 1 x 2 Y f = l(y v,f + H(f; f 0)+g v,y n,f + g n) p f (y; g v,g n) 4 c 2010 Information Processing Society of Japan
5 p f (y; g v,g n) N(y; μ f (θ v,θ n),σf 2 (θ v,θ n)) (13) μ f (g v,g n)=log(exp(μ v,f + H(f; f 0)+g v)+exp(μ n,f + g n)) (14) σ 2 f (g v,g n)= (exp (μ v,f + H(f; f 0)+g v)) 2 σ 2 v,f +(exp(μ n,f + g n)) 2 σ 2 n,f (exp (μ v,f + H(f; f0)+gv)+exp(μ n,f + g n)) 2 (15) N (y; μ, σ 2 ) μ σ SIR (g v,g n) BFGS Broyden-Fletcher-Goldfarb- Shanno Q(g v,g n) Q(g v,g n)= log N (y(f); u f (g v,g n),σf 2 (g v,g n)) (16) y(f) f y(f) g v g n p f (y; g v,g n) μ v,f σv,f 2 ˆμ v,f ˆσ v,f 2 pˆ f (y; g v,g n) y(f) y(f) ˆ ŷ(f) =y(f)+ζ(f) (17) ζ(f) =E y[ˆp f (y; g v,g n)] E y[p f (y; g v,g n)] (18) E[ ] ζ(f) ĝ v ŷ(f) (17) 10ms ŷ(f) b ζ(f) b ζ(f) (17) ŷ(f) b ζ(b, a) Ŵ (b, a) W (b, a) Ŵ (b, a) = ( W (b, a) + ζ(b, a)) (19) W (b, a) a f (4) ˆx(t) ICWT ˆx(f) = 1 ( ) 1 t b 1 Ŵ (b, a) Ψ dadb (20) C Ψ a a a2 C Ψ ICWT CWT FFT ) 14) f M m(1,,m) a v,m b v,m ψ v,m μ v,m σv,m 2 15) G m(f; ψ v,m,μ v,m,σv,m) 2 ψ v,mn (f; μ v,m,σv,m) 2 = M ψ m =1 m N (f; μ (21) v,m,σ2 v,m ) ψ v,m ψ v,m 0 M ψv,m =1 m=1 μ v,m σv,m 2 5 c 2010 Information Processing Society of Japan
6 μ v,f σv,f 2 M μ v,f = G m(f; ψ v,m,μ v,m,σv,m)(a 2 v,mf + b v,m) (22) σ 2 v,f = m=1 M G m(f; ψ v,m,μ v,m,σv,m) 2 2 βv,m 2 (23) m=1 M M 10 EM Expectation and Maximization {ψ n,m,μ n,m,σn,m,a 2 n,m,b n,m,βn,m} /a/ /a/ 1 I s i(i =1,...,I) h f i,h y i,h s i = {(f i,1,y i,1),...(f i,h,y i,h ),...(f i,hi,y i,hi )} (24) H I i log N (y i,h + k i; μ v,fi,h,σv,f 2 i,h ) (25) i h k i k i Step 0 k i =0 Step 1 Step 2 Step 3 EM k i Hi μ v,fi,h y i,h h=1 σv,f 2 i,h k i = 1 Hi h=1 (26) 1 σv,f 2 i,h k i ( 60Hz 7500Hz) M m (f i,h,y i,h ) a m b m f i,h μ m σm 2 ψ m 1 M s i(i =1,...,I) Step 1 EM 1 Step I y 1(f),,y i(f),,y I(f) θ v = {ψ v,m,μ v,m,σv,m,a 2 v,m,b v,m,βv,m} 2 θ n = {ψ n,m,μ n,m,σn,m,a 2 n,m,b n,m,βn,m} 2 i μ v,f,i = μ v,f + H(f; f 0(i)) (27) i F0 f 0(i) i 1 (13) (15) θ v θ n p i,f (y; θ v,θ n,g i,v,g i,n) 1 L i i 6 c 2010 Information Processing Society of Japan
7 L = = I log p i,f (y; θ v,θ n,g i,v,g i,n)df (28) I log( yi (f) N (log(exp(y i(f)) exp(u)); μ v,f,i + g i,v,σ 2 v,f) N (U; μ n,f + g i,n,σn,f 2 exp(y i(f)) ) du)df (29) exp(y i(f)) exp(u) I yi (f) = log( N (log(exp(y i(f)) exp(u)); μ n,f + g i,n,σn,f 2 ) N (U; μ v,f,i + g i,v,σv,f) 2 exp(y i(f)) du)df (30) exp(y i(f)) exp(u) g i,v g i,n 3.2 k i SIR f {g i,v,g i,n,θ v,θ n} g i,n θ n (29) g i,v θ v g i,v θ v (30) g i,n θ n g i,n θ n (29) U g i,v θ v N (U; μ n,f + g i,n,σn,f 2 ) U = y i(f) i f R (U i,1,f,,u i,r,f,,u i,r,f ) L I R L log π i,r,f N (log(exp(y i(f)) exp(u i,r,f )); μ v,f,i + g i,v,σv,f) 2 (31) r=1 exp(y i(f)) π i,r,f = (exp(y i(f)) exp(u i,r,f )) R y i (f) N (U; μ n,f + g i,n,σn,f 2 )du (32) R 300 g i,n θ n π i,r,f log(exp(y i(f)) exp(u i,r,f )) (31) g i,v θ v g i,v θ v (29) (31) g i,n θ n (31) EM (31) λ = {g i,v,θ v} λ z i,r,f π i,r,f N (log(exp(y i(f)) exp(u i,r,f )); μ v,f,i + g i,v,σv,f) 2 z i,r,f = R π r =1 i,r,f N (log(exp(y i(f)) exp(u i,r,f )); μ v,f,i + g i,v,σv,f 2 ) (33) λ z i,r,f z i,r,f z i,r,f Q 1(λ λ ) Q 1(λ λ )= I r=1 R z i,r,f log π i,r,f N (log(exp(y i(f)) exp(u i,r,f )); μ v,f,i + g i,v,σ 2 v,f)df (34) λ λ z i,r,f L (34) π i,r,f Q 2(λ λ ) I R Q 2(λ λ )= z i,r,f log N (log(exp(y i(f)) exp(u i,r,f )); μ v,f,i + g i,v,σv,f)df 2 r=1 (35) Q 1(λ λ ) Q 2 z (25) Q Step 0 g i,v =0 g i,n =0 Step 1 g i,n θ n (29) U Step 2 U g i,v θ v (33) z i,r,f 7 c 2010 Information Processing Society of Japan
8 Step 3 z i,r,f (35) Q Step 4 Step 2 3 Step 5 Step 2 Step 5 g i,v θ v (30) U Step 6 U g i,n θ n (33) z i,r,f Step 7 z i,r,f (35) Q Step 8 Step 2 3 Step 9 Step 6 Step 9 Step 1 8 Step RWC RWC-MDB-P ) No (a) 3.3 4(b) 3.3 4(c) (a) (b) (a) (c) 4 (b) (a) (c) (a) (a) 3.2 節の手法による単独歌唱からの推定結果 7000 (b) 提案法による推定結果 7000 (c) 混合音から抽出した調波構造からの3.2 節の手法による推定結果 RWC RWC-MDB-P ) No.7 /i/ 5 No.7 5(a) 5(b) No.13 5(c) No.20 5(d) 6 5 No /i/ ĝ v 400Hz Hz Hz 8 c 2010 Information Processing Society of Japan
9 (a) 元のスペクトル (No.7) 7000 (b) ボーカルキャンセル (c) No. 13 の歌手に変換 (d) No. 20 の歌手に変換 RWC RWC-MDB-P ) No.7 (b) (c)no.13 (d)no Hz 5. W- PST 1) (a) 元のスペクトル (No.7) を表現する (b) No. 20の歌手の歌声包絡テンプレートを用いて 合成されたスペクトルテンプレート 操作されたスペクトルテンプレート RWC RWC-MDB-P ) No.7 (a) No.20 (b) (a) 5(a) (b) 5(d) F0 CrestMuse (JST CREST) RWC RWC-MDB-P ) 1) Vol.2009-MUS-81 (2009). 2) Goto, M.: Active Music Listening Interfaces Based on Signal Processing, Proceedings of the 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), pp.iv (2007). 3) Yoshii, K., Goto, M., Komatani, K., Ogata, T. and Okuno, H.G.: Drumix: An Audio Player with Real-time Drum-part Rearrangement Functions for Active Music 9 c 2010 Information Processing Society of Japan
10 Listening, IPSJ Journal, Vol.48, No.3, pp (2007). 4) Itoyama, K., Goto, M., Komatani, K., Ogata, T. and Okuno, H.G.: Instrument Equalizer for Query-by-Example Retrieval: Improving Sound Source Separation based on Integrated Harmonic and Inharmonic Models, Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR 2008), pp (2008). 5) Bonada, J., Celma, O., Loscos, A., Ortola, J., Serra, X., Yoshioka, Y., Kayama, H., Hisaminato, Y. and Kenmochi, H.: Singing Voice Synthesis Combining Excitation plus Resonance and Sinusoidal plus Residual Models, Proceedings of International Computer Music Conference (2001). 6) VOCALOID Vol.2007-MUS-72, pp (2007). 7) Vol.45, No.3, pp (2004). 8) Sinsy - HMM-based Singing Voice Synthesis System: 9) Stylianou, Y., Cappé, O. and Moulines, E.: Continuous probabilistic transform for voice conversion, IEEE Transactions on Speech and Audio Processing, No.2, pp (1998). 10) Mouchtaris, A., der Spiegel, J.V. and Mueller, P.: Nonparallel training for voice conversion based on a parameter adaptation approach, IEEE Transactions on Audio, Speech and Language Processing, Vol.14, No.3, pp (2006). 11) Toda, T., Black, A.W. and Tokuda, K.: Voice conversion based on maximum likelihood estimation of spectral parameter trajectory, IEEE Transactions on Audio, Speech and Language Processing, Vol.15, No.8, pp (2007). 12) Vol.48, No.12, pp (2007). 13) No.282, pp (2007). 14) Jacobs, R.J., Jordan, M., Nowlan, S.J. and Hinton, G.E.: Adaptive mixtures of local experts, Neural Computation, Vol.3, pp (1991). 15) Xu, L., Jordan, M. I. and Hinton, G. E.: An alternative model for mixtures of experts, Advances in Neural Information Processing Systems 7, pp (1994). 16) RWC : Vol.45, No.3, pp (2004) (31) L Jensen ( I R ) π i,r,f N (x i,r,f ; μ v,f,i + g i,v,σv,f) 2 L(λ) = log df (36) I R r=1 r=1 z i,r,f x i,r,f z i,r,f z i,r,f log π i,r,f N (x i,r,f ; μ v,f,i + g i,v,σv,f) 2 z i,r,f df = F (λ λ ) (37) x i,r,f = log(exp(y i(f)) exp(u i,r,f )) (38) L(λ) L(λ )=F (λ λ ) F (λ λ )+ I z i,r,f log ( zi,r,f z i,r,f ) df (39) F (λ λ ( 1) ) λ L(λ) I Q 1(λ λ )=F(λ λ )+ z i,r,f log ( z i,r,f) df (40) λ F (λ λ ) λ Q 1(λ λ ) λ Q(λ λ ) L(λ) 10 c 2010 Information Processing Society of Japan
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NTT コミュニケーション科学基礎研究所 石黒勝彦 2013/01/15-16 統計数理研究所会議室 1 1 画像認識系から尐し遅れますが 最近では音声 音響データに対してもトピックモデルが利用されるようになっています 2 1. どの特徴量を利用するか? 2. 時系列性をどう扱うか? 3 どの特徴量を利用して どうやって BoW 形式に変換するかを検討する必要があります MFCC: 音声認識などで広い範囲で利用される
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1,a) 1,b) 1,c) 2012 11 8 2012 12 18, 2013 1 27 WEB Ruby Removal Filters Using Genetic Programming for Early-modern Japanese Printed Books Taeka Awazu 1,a) Masami Takata 1,b) Kazuki Joe 1,c) Received: November
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1,a) 2,b) 4,c) 3,d) 4,e) Web A Review Supporting System for Whiteboard Logging Movies Based on Notes Timeline Taniguchi Yoshihide 1,a) Horiguchi Satoshi 2,b) Inoue Akifumi 4,c) Igaki Hiroshi 3,d) Hoshi