log F0 意識しゃべり葉の log F0 Fig. 1 1 An example of classification of substyles of rap. ' & 2. 4) m.o.v.e 5) motsu motsu (1) (2) (3) (4) (1) (2) mot

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1 HMM Rap-style Singing Voice Synthesis Keijiro Saino, 1 Keiichiro Oura, 2 Makoto Tachibana, 1 Hieki Kenmochi 3 an Keiichi Tokua 2 This paper aresses rap-style singing voice synthesis. Since it has not been very clear how to write a musical score for rap-style songs, existing singing voice synthesis systems base on musical scores are not suitable for synthesizing them with an intuitive input. Here a new type of musical score specialize for a rap-style is efine. An HMM-base singing voice synthesis system is use to realize an automatic synthesis of realistic rap-style singing. Glissano phenomenon which is special for the style coul be foun in synthesis results. It was also trie to apply pitch parameters generate from the HMMs to a sample-concatenation-base singing voice synthesis system. 1) HMM 2) VOCALOID 3) 2 3 HMM 4 5 HMM 6 1 Corporate Research an Development Center, Yamaha Corporation 2 Department of Computer Science an Engineering, Nagoya Institute of Technology 3 yamaha+ yamaha+ Division, Yamaha Corporation 1 c 2012 Information Processing Society of Japan

2 log F0 意識しゃべり葉の log F0 Fig. 1 1 An example of classification of substyles of rap. ' & 2. 4) m.o.v.e 5) motsu motsu (1) (2) (3) (4) (1) (2) motsu (1), (2), (4) motsu 2 (1), (2) motsu (1) (2) メロディ構造の意識のない, しゃべり葉のようなイントネーション Fig. 2 2 motsu Examples of log F 0 series of rap-style singing voice by motsu. motsu (4) (1) (4) (1), (2) 2.2 (1) (2) 2 c 2012 Information Processing Society of Japan

3 C#(+5) B(+3) G#(root) F#(-2) D#(-5) ぶし ʼ (A) (B) 2 下降向 3. HMM つめ以降の符 ( レッツ ʼ グリッサンド符は通常符と同様の単位で, 任意のさをもちうる 3 Fig. 3 The efine musical notation rules. motsu 2.1 motsu , -2, 0, +3, +5 1 VOCALOID 3) HMM 2) HMM 2.2 HMM HMM 2 HMM HMM 6) 3 c 2012 Information Processing Society of Japan

4 1 Table 1 Singing voice ata use for moel training. / motsu BPM motsu 48kHz/16bit 49 STRAIGHT 5 ms SWIPE 7) 5 ms & ' Root = C#3 HMM HMM MLSA HMM motsu motsu motsu 13 motsu 1 (1) (4) (1), (2) motsu motsu (BPM 128) Fig. 4 A part of input rap score an contour of generate log F 0. (BPM 128) HMM (Hien Semi-Markov Moels; HSMM) 8) left-to-right 5 HMM 4 HMM c 2012 Information Processing Society of Japan

5 Table 2 2 Subjective evaluation methos. Root = C#3 Root = C#3 A B C D ' el 信頼度区間 DMOS ' Fig. 5 5 Subjective evaluation results. 6 (BPM 100) Fig. 6 Generate log F 0 contour on each experimental conition (BPM 100) (Degraation Mean Opinion Score; DMOS) A D /a/ /o/ /a/ 5. HMM VOCALOID 3) 4 5 c 2012 Information Processing Society of Japan

7 HMM VOCALOID Fig. 7 An example of VOCALOID pitch parameters converte from the parameters generate from the HMMs. VOCALOID VOCALOID VOCALOID 7 HMM VOCALOID HMM VocaListener 9) 6.

6 7 HMM VOCALOID Fig. 7 An example of VOCALOID pitch parameters converte from the parameters generate from the HMMs. VOCALOID VOCALOID VOCALOID 7 HMM VOCALOID HMM VocaListener 9) 6. HMM 2 VOCALOID HMM 7. motsu 1) H. Kenmochi, VOCALOID an Hatsune Miku phenomenon in Japan, Proc.InterSinging 2010, pp.1 4, ) K.Oura, A.Mase, T.Yamaa, S.Muto, Y.Nankaku, an K.Tokua, Recent Development of the HMM-base Singing Voice Synthesis System - Sinsy, Proc.SSW7, pp , ) H.Kenmochi an H.Ohshita, VOCALOID-Commercial Singing Synthesizer Base on Sample Concatenation, Proc.INTERSPEECH 2007, pp , ) [DVD BOOK], (2005). 5) M.O.V.E Official Website, 6),,,, HMM,, vol.i, , pp , ) A.Camacho, SWIPE: A Sawtooth Waveform Inspire Pitch Estimator for Speech an Music, Ph.D.Thesis, University of Floria, ) H.Zen, T.Masuko, K.Tokua, T.Kobayashi, an T.Kitamura, A Hien Semi- Markov Moel-Base Speech Synthesis System, Proc.IEICE Trans., vol.90-d, no.5, pp , ) T. Nakano, an M. Goto, VocaListener: A Singing-to-Singing Synthesis System Base on Iterative Parameter Estimation, Proc.SMC pp , c 2012 Information Processing Society of Japan

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2 CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for

log F0 意識 しゃべり 葉の log F0 Fig. 1 1 An example of classification of substyles of rap. ' & 2. 4) m.o.v.e 5) motsu motsu (1) (2) (3) (4) (1) (2) mot

log F0 意識しゃべり葉の log F0 Fig. 1 1 An example of classification of substyles of rap. ' & 2. 4) m.o.v.e 5) motsu motsu (1) (2) (3) (4) (1) (2) mot