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1 48% 82% Identification of Hihat Cymbals for Musical Audio Signals Using the Single Template Adaptation Method KAZUYOSHI YOSHII,MASATAKA GOTO and HIROSHI G. OKUNO Dept. of Intelligence Science and Technology, Graduate School of Infomatics, Kyoto University National Institute of Advanced Industrial Science and Technology (AIST) This paper describes the identification of hihat cymbals for real-world polyphonic musical audio signals. The most critical problem with percussive sound identification is that acoustic features of those sounds vary with each musical piece, and thus we cannot prepare their precise sound templates in advance. To solve this problem, we developed the single template adaptation method which could be applied to spectra of bass and snare drums. In this paper, we aim to show the effectiveness of our single template adaptation method in the identification of hihat cymbals. For this purpose, we introduce a quantization process at a lower time- resolution for those spectrum. Experimental results showed that the average accuracy of identifying hihat cymbals in popular music is improved from around 48% to around 82% by the single template adaptation method )
2 3 3) 6) 1) MPEG-7 2) MPEG-7 (A) (B) 2 9) Zils 10) (1) (2) 2.1 2
3 音楽音響信号 発音時刻粗探索ステージ P1 P19 P30 P31 P47 P62 P85 PN テンプレート更新ステージ中央値中央値 T S T 0 T 1 T 2 T A スペクトル断片 スペクトル断片選択ステージ 種テンプレート 反復適応 適応後テンプレート 1 STFT P (t, f) t, f Q(t, f) P (t, f) P (t, f) 44.1kHz [Hz] [ms] STFT (1) 3 t = a 1, a,a+1 P(t, f)/ t > 0 a Q(a, f) P(t, f) Q(a, f) = (1) t t=a Q(a, f) =0 3
4 通過率 F( f ) 1.0 large 種テンプレート ( 実線 ) 距離大 ハイハットシンバル音を含むスペクトル断片 ( 破線 ) 周波数ビン f ブロックに分割 2 (2) t S(t) Q(t, f) small 距離を適切に評価 ブロック内のパワーの和を計算 2048 S(t) = F (f) Q(t, f) (2) f=1 F (f) 2 (3) S(t) S(t) Savitzky Golay 8) T S 1 T S STFT T S T S (t, f) (1 t 15 [frames], 1 f 2048 [bins]) g T g T S T 0 T S P i (i =1,,N) o i [ms] N P i T g F (f) T g Ṕi T g (t, f) =F (f) T g (t, f) (3) Ṕ i (t, f) =F (f) P i (t, f) (4) bins 0 2frames 15frames frame 2048bins ブロック内のパワーの和 2.4 T S [frames] (20 [ms]) 25 [bins] (269 [Hz]) 4
5 = T 0 (T S ) P i ˆD i ˆD i = ( ˆT0 (t, f) ˆP ) 2 i (t, f) (5) ˆt=1 ˆf=1 ˆT 0 (t, f) ˆP i (t, f) ˆT 0 (t, f) = 1 50 ˆP i (t, f) = t 25f t =2t 1 f =25f 24 2t 25f t =2t 1 f =25f 24 T 0 (t,f ) (6) Ṕ i (t,f ) (7) - 2[frames] 25[bins] 2 D i D i = ( ) 2 ˆTg (t, f) Ṕi(t, f) (g 1) (8) ˆt=1 ˆf=1 ˆT g 2.5 T g T g+1 5 ˆT g+1 (t, f) =medianˆp s (t, f) (9) s P s (s =1,,M) M ˆP s ˆT g+1 ˆP s (t, f) = t 25f t =2t 1 f =25f 24 Ṕ s (t,f ) (10) 3. 5) - 6 5
6 P1 P19 P30 P31 P47 P62 P85 PN no 各スペクトル断片がテンプレートを含んでいるか? 重み関数生成ステージ 特徴点 T A no スペクトル断片 P 47 スペクトル断片 テンプレートT A テンプレートを含んでいる場合 P 62 音量補正ステージ距離計算ステージ テンプレート T A スペクトル断片 適応後テンプレート テンプレートを含んでいない場合 t f w w(t, f) = ˆT A (t, f) (11) ˆT A F (f) 3.2 ˆT A ˆP i ˆT A w η i δ i 7 η i ˆP i ˆT A ˆT A ˆP i 6 i δ i 7 Pˆi TˆA Lη t, ) L η t, ) Lη t, ) L i ( f t, 341 frame t TˆA i ( f t, 199 i ( f t, 13 フレーム t の特徴点におけるパワー差を計算 the first quantile δ i (t) フレーム t におけるパワー差を計算 t δ i (t) η i (t, f t,k ) (1) f t,k (k =1,, 500) ˆT A f t,k t w(t, f t,k ) k η i (t, f t,k ) η i (t, f t,k )= ˆP i (t, f t,k ) ˆT A (t, f t,k ) (12) t 6
7 (2) t δ i (t) η i (t, f t,k ) δ i (t) =first-quantile k η i (t, f t,k ) (13) δ i (t) k K i (t) δ i (t) Ψ R δ ˆT A ˆP i Ψ (3) i {t δ i = i (t)>ψ} δ i(t) w(t, f t,ki (t)) {t δ i (t)>ψ} w(t, f (14) t,k i (t)) i Θ ˆT A ˆP i Θ ˆP i ˆP i (t, f) = ˆP i (t, f) i (15) 3.3 ˆT A ˆP i ˆP i ˆT A ˆP i (t, f) ˆT A (t, f) ˆP i (t, f) ˆT A (t, f) ˆP i (t, f) { 0 if Ψ ˆP γ i (t, f) = i (t, f) ˆT A (t, f) Ψ (16) 1 otherwise γ i (t, f) ˆT A ˆP i t f Ψ ˆP i (t, f) ˆT A (t, f) γ i (t, f) 0 ˆP i (t, f) ˆT A (t, f) ˆP i (t, f) γ i (t, f) 1 Γ i - γ i w Γ i = w(t, f) γ i (t, f) (17) t=1 f=1 P i Γ i Θ Γ Γ i < Θ Γ 25% RWC-MDB-P ) 10 1 CD RWC-MDB-I ) 421HHCC3.WAV 16bit 44.1kHz 30 [ms] 2 MIDI F = = 2 F = adapt base base 1 1 R δ Ψ Θ Θ Γ method [frames] [db] [db] base adapt
8 2 10 piece base method (baseline) adapt method (proposed) number recall rate precision rate F measure recall rate precision rate F measure No % (55/436) 82 % (55/67) % (345/436) 81 % (345/424) 0.80 No % (77/88) 97 % (77/79) % (88/88) 83 % (88/106) 0.91 No % (177/182) 76 % (177/233) % (149/182) 100 % (149/149) 0.90 No % (108/114) 74 % (108/145) % (92/114) 85 % (92/108) 0.83 No % (59/184) 52 % (59/114) % (181/184) 54 % (181/334) 0.70 No. 44 2%(4/235) 44 % (4/9) % (216/235) 57 % (216/278) 0.70 No % (41/179) 87 % (41/47) % (169/179) 73 % (169/230) 0.83 No % (163/181) 74 % (163/221) % (153/181) 94 % (153/162) 0.89 No % (17/271) 55 % (17/31) % (267/271) 86 % (267/312) 0.92 No % (45/183) 43 % (45/105) % (179/183) 74 % (179/241) 0.84 average 36.3 % (746/2053) 71.0 % (746/1051) % (1839/2053) 75.2 % (1839/2444) adapt F adapt base No. 44 No. 52 adapt F No 30 No. 44 F 5. CD (A) COE 1) Dixon, S., Pampalk, E. and G. Widmer, G.: Classification of Dance Music by Periodicity Patterns, ISMIR, pp (2003). 2) Gómez, E., Gouyon, F., Herrera, P. and Amatriain, X.: Using and enhancing the current MPEG-7 standard for a music content processing tool, AES (2003). 3) Goto, M.: An Audio-based Real-time Beat Tracking System for Music With or Without Drum-sounds, Journal of New Music Research, Vol. 30, No. 2, pp (2001). 4),,, : RWC :,, Vol. 45, No. 3, pp (2004). 5), :, D-II, Vol.J77-D-II, No.5, pp (1994). 6) Gouyon, F. and Herrera, P.: Determination of the meter of musical audio signals: Seeking recurrences in beat segment descriptors, AES (2003). 7) Pampalk, E., Dixon, S. and Widmer, G.: Exploring Music Collections by Browsing Different Views, ISMIR, pp (2003). 8) Savitzky, A. and Golay, M.: Smoothing and Differentiation of Data by Simplified Least Squares Procedures, J. of Analytical Chemistry, Vol.36, No.8, pp (1964). 9),, :,, MUS , pp (2003). 10) Zils, A., Pachet, F., Delerue, O. and Gouyon, F.: Automatic Extraction of Drum Tracks from Polyphonic Music Signals, WEDELMUSIC, pp (2002). 8
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