樂器演奏依賴於協調的身體動作。藉由有序的規律訓練，以讓音樂家實現完美的演出。因此，為了瞭解對玩家感官的動作表現進行程式設計，只有從音頻信號中蒐集相關資訊是不夠的。近日，研究音樂家在演奏期間的肌肉活動表現已經讓我們產生了興趣。在這項研究中，我們提出了一個多通道系統，此系統能夠同時記錄音頻聲音和肌電圖（EMG）信號，並發展出新的演算法來分析音樂表現以及探討音樂訊號與音樂家演奏動作之間的關係。首先，利用音頻聲音對動作部分進行資訊識別，並且使用肌電圖信號偵測小提琴在演奏時的。接著，我們將介紹六個特徵值，並且使用它們來揭示小提琴演奏期間肌肉活動的變化。除此之外，加上額外的音頻信號資訊，這樣提出的作法將能夠有效地截取演奏時的弓法週期，並且在小提琴演奏時偵測演奏者的肌電圖訊號變化，進而得知小提琴弓法的方向。因此，這項提出的系統可以提供一個更好的方式來理解音樂家在演奏的時候是如何激發肌肉群，以及組織多關節運動的演奏動作。

Playing a music instrument relies on the harmonious body movements. Motor sequences are trained to achieve the perfect performances in musicians. Thus, the information from audio signal is not enough to understand the sensorimotor programming in players. Recently, the investigation of muscular activities of players during performance has attracted our interests. In this work, we propose a multi-channel system that records the audio sounds and electromyography (EMG) signal simultaneously and also develop algorithms to analyze the music performance and discover its relation to player’s motor sequences. The movement segment was first identified by the information of audio sounds, and the direction of violin bowing detected by the EMG signal. Six features were introduced to reveal the variations of muscular activities during violin playing. With the additional information of the audio signal, the proposed work could efficiently extract the period and detect the direction of motor changes in violin bowing. Therefore, the proposed work could provide a better understanding of how players activate the muscles to organize the multi-joint movement during violin performance.

[1] D. C. Harding, K. D. Brandt, and B. M. Hillberry: “Minimization of finger joint forces and tendon tensions in pianists,” Med. Probl. Perform Art pp.103-104, 1989.
[2] K. C. Engel, M. Flanders, and J. F. Soechting: “Anticipatory and sequential motor control in piano playing,” Exp Brain Res. pp. 189-199, 1997.
[3] D. Parlitz, T. Peschel, and E. Altenmuller: “Assessment of dynamic finger forces in pianists: Effects of training and expertise,” J. Biomech. pp.1063-1067, 1998.
[4] H. Kinoshita , S. Furuya , T. Aoki, and E. Altenmüller: “Loudness control in pianists as exemplified in keystroke force measurements on different touches,” J Acoust Soc Am. pp. 2959-69, 2007.
[5] A. E. Minetti, L. P. Ardigò, and T. McKee: “Keystroke dynamics and timing: Accuracy, precision and difference between hands in pianist's performance,” J Biomech. pp. 3738-43, 2007.
[6] R. Merletti and P. Parker: “Electromyography: physiology, engineering, and noninvasive applications,” Wiley-IEEE Press, 2004.
[7] C. J. Lai, R. C. Chan, and T. F. Yang et al: “EMG changes during graded isometric exercise in pianists: comparison with non-musicians,” Journal of the Chinese Medical Association, vol. 71, no. 11, pp. 571-575, 2008.
[8] M. Candidi, L. M. Sacheli, and I. Mega et al: “Somatotopic mapping of piano fingering errors in sensorimotor experts: TMS studies in pianists and visually trained musically naïves,” Cerebral Cortex, vol. 24, no. 2, pp. 435-443, 2014.
[9] S. Furuya, T. Aoki, and H. Nakahara et al: “Individual differences in the biomechanical effect of loudness and tempo on upper-limb movements during repetitive piano keystrokes,” Human movement science, vol. 31, no. 1, pp. 26-39, 2012.
[10] D. L. Rickert and M. Halaki, K. A. Ginn et al: “The use of fine-wire EMG to investigate shoulder muscle recruitment patterns during cello bowing: The results of a pilot study,” Journal of Electromyography and Kinesiology, vol. 23, no. 6, pp. 1261-1268, 2013.
[11] A. Fjellman-Wiklund and H. Grip, J. S. Karlsson et al: “EMG trapezius muscle activity pattern in string players: Part I—is there variability in the playing technique?,” International journal of industrial ergonomics, vol. 33, no. 4, pp. 347-356, 2004.
[12] J. G. Bloemsaat, R. G. Meulenbroek, and G. P. Van Galen: “Differential effects of mental load on proximal and distal arm muscle activity,” Experimental brain research, vol. 167, no. 4, pp. 622-634, 2005.
[13] S. Fujii and T. Moritani: “Spike shape analysis of surface electromyographic activity in wrist flexor and extensor muscles of the world's fastest drummer,” Neuroscience letters, vol. 514, no. 2, pp. 185-188, 2012.
[14] S. Furuya and H. Kinoshita: “Organization of the upper limb movement for piano key-depression differs between expert pianists and novice players,” Experimental brain research, vol. 185, no. 4, pp. 581-593, 2008.
[15] P. Mazurkiewicz: “Automatic Segmentation of EMG Signals Based on Wavelet Representation,” Advances in Soft Computing Volume 45, 2007, pp. 589-595
[16] M. Chendeb, M. Khalil, J. Duchêne: “The use of wavelet packets for event detection,” 13th European Signal Processing Conference (EUSIPCO), Antalya, September 2005.
[17] C. I. Christodoulou and C. S. Pattichis: “Unsupervised pattern recognition for the classification of EMG signals,” Biomedical Engineering, IEEE Transactions on Volume 45, pp.169-178, 1999.
[18] R. Gut and G.S. Moschytz: “High-precision EMG signal decomposition using communication techniques,” Signal Processing, IEEE Transactions on Volume 48, pp. 2487–2494, 2000.
[19] G. Kaur, A. S. Arora, and V. K. Jain, “Comparison of the techniques used for segmentation of EMG signals," In Proceedings of the 11th WSEAS international conference on Mathematical and computational methods in science and engineering, pages 124–129. 2009.
[20] L. Fraiwan, M. Awwad, M. Mahdawi, S. Jamous: “Real time virtual prosthetic hand controlled using emg signals,” 1st Middle East Conference of Biomedical Engineering (MECBME), 2011.
[21] A. Holzapfel, Y. Stylianou, A. C. Gedik et al.: “Three dimensions of pitched instrument onset detection,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 18, no. 6, pp. 1517-1527, 2010.
[22] E. Benetos, and Y. Stylianou, “Auditory spectrum-based pitched instrument onset detection,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 18, no. 8, pp. 1968-1977, 2010.
[23] W. J. J. Boo, Y. Wang, and A. Loscos, “A violin music transcriber for personalized learning,” pp. 2081-2084, IEEE International Conference on Multimedia and Expo, 2006.
[24] J. P. Bello, L. Daudet, S. Abdallah et al., “A tutorial on onset detection in music signals,” in Speech and Audio Processing, IEEE Transactions on, pp. 1035-1047, 2005.
[25] Bodybuilding is lifestyle! (2003) “Chest - Bodybuilding is lifestyle!” http://www.bodybuildingislifestyle.com/chest/.
[26] Bodybuilding is lifestyle! (2003) “Chest - Bodybuilding is lifestyle!” http://www.bodybuildingislifestyle.com/hamstrings/.
[27] B. E. Boser, I. M. Guyon and V. N. Vapnik: “A training algorithm for optimal margin classifiers,” In Fifth Annual Workshop on Computational Learning Theory, ACM, 1992.
[28] A. J. Andrews: “Finger movement classification using forearm EMG signals,” M. Sc. dissertation, Queen's University, Kingston, ON, Canada, 2008.