Abstract
In this paper, an augmented reality application for drumkit simulation is presented. The system is capable of classifying any percussive sounds produced by the user from an everyday desktop environment, e.g. clapping, snapping, stroking different objects with a pencil, etc., recognizing up to six different classes of drum hits. These different types of user-generated sounds will subsequently be associated to predefined drumkit sounds, resulting in a natural and intuitive audio interface for drummers and percussionists, which only requires a computer with a built-in microphone. A set of audio features and classification techniques are evaluated for the implementation of the aforementioned system.
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References
Agostini G, Longari M, Pollastri E (2001) Musical instrument timbres classification with spectral features. In: IEEE fourth workshop on multimedia signal processing, 2001. pp 97–102
Antle A, Droumeva M, Corness G (2008) Playing with the sound maker: do embodied metaphors help children learn? In: Proceedings of the 7th international conference on Interaction design and children. ACM, pp 178–185
Bakanas P, Armitage J, Balmer J, Halpin P, Hudspeth K, Ng K (2012) mConduct: Gesture transmission and reconstruction for distributed performance. In: ECLAP 2012 Conference on information technologies for performing arts, media access and entertainment. Firenze University Press, p 107
Bakker S, van den Hoven E, Antle A (2011) Moso tangibles: evaluating embodied learning. In: Proceedings of the fifth international conference on Tangible, embedded, and embodied interaction. ACM, pp 85–92
Basili R, Serafini A, Stellato A (2004) Classification of musical genre: a machine learning approach. In: Proceedings of ISMIR. Citeseer
Borchers J, Lee E, Samminger W, Mühlhäuser M (2004) Personal orchestra: A real-time audio/video system for interactive conducting. Multimedia Systems 9(5):458–465
Bradshaw D, Ng K (2008) Analyzing a conductor’s gestures with the wiimote. In: Proceedings of EVA London 2008: the International Conference of Electronic Visualisation and the Arts
Breebaart J, Mckinney M (2002) Features for audio classification. In:in Proceedings of the Philips Symposium of Intelligent Algorithms, Eindoven
Brown J (1999) Computer identification of musical instruments using pattern recognition with cepstral coefficients as features. J Acoust Soc Am 105:1933
Castellano G, Bresin R, Camurri A, Volpe G (2007) Expressive control of music and visual media by full-body movement. In:Proceedings of the 7th international conference on new interfaces for musical expression. ACM, pp 390–391
De Dreu M, Van der Wilk A, Poppe E, Kwakkel G, Van Wegen E (2012) Rehabilitation, exercise therapy and music in patients with parkinson’s disease: a meta-analysis of the effects of music-based movement therapy on walking ability, balance and quality of life. Parkinsonism Relat Disord 18:S114—S119
Deng J, Simmermacher C, Cranefield S (2008) A study on feature analysis for musical instrument classification. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics 38(2):429–438. doi:10.1109/TSMCB.2007.913394
Eronen A (2001) Comparison of features for musical instrument recognition. In: Proceedings IEEE workshop on applications of signal processing to audio and acoustics
Essl G., Rohs M. (2009) Interactivity for mobile music-making. Organised Sound 14(02):197–207
Gillet O, Richard G (2004) Automatic transcription of drum loops. In: IEEE international conference on acoustics, speech, and signal processing, 2004. Proceedings. (ICASSP ’04). vol 4. pp iv–269 – iv–272 vol 4. doi:10.1109/ICASSP.2004.1326815
Gouyon F, Pachet F, Delerue O (2000) On the use of zero-crossing rate for an application of classification of percussive sounds. In: Proceedings of the COST G-6 conference on digital audio effects (DAFX-00)
Gower L, McDowall J (2012) Interactive music video games and children’s musical development. Br J Music Educ 29(01):91–105
Halpern M, Tholander J, Evjen M, Davis S, Ehrlich A, Schustak K, Baumer E, Gay G (2011) Moboogie: creative expression through whole body musical interaction. In:Proceedings of the 2011 annual conference on Human factors in computing systems. ACM, pp 557–560
Herrera P, Yeterian A, Gouyon F (2002) Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques. Music and Artif Intell: 69–80
Herrera-Boyer P, Peeters G, Dubnov S (2003) Automatic classification of musical instrument sounds. J New Music Res 32(1):3–21
Holland S, Bouwer A, Dalgelish M, Hurtig T (2010) Feeling the beat where it counts: fostering multi-limb rhythm skills with the haptic drum kit. In:Proceedings of the fourth international conference on Tangible, embedded, and embodied interaction. ACM, pp 21–28
Höofer A, Hadjakos A, Mühlhäuser M (2009) Gyroscope-Based Conducting Gesture Recognition. In: Proceedings of the international conference on new interfaces for musical expression. pp 175–176 . http://www.nime.org/proceedings/2009/nime2009_175.pdf
Ihara M, Maeda S, Ishii S (2007) Instrument identification in monophonic music using spectral information. In: IEEE international symposium on signal processing and information technology, 2007. pp 595–599
Je H, Kim J, Kim D (2007) Hand gesture recognition to understand musical conducting action. In: The 16th IEEE International Symposium on Robot and Human interactive Communication, 2007. RO-MAN 2007. IEEE, pp 163–168
Jordà S (2010) The reactable: tangible and tabletop music performance. In: Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems. ACM, pp 2989–2994
Khoo E, Merritt T, Fei V, Liu W, Rahaman H, Prasad J, Marsh T (2008) Body music: physical exploration of music theory. In: Proceedings of the 2008 ACM SIGGRAPH symposium on video games. pp. 35–42
Lee E, Nakra T, Borchers J (2004) You’re the conductor: a realistic interactive conducting system for children. In:Proceedings of the 2004 conference on new interfaces for musical expression. National University of Singapore, pp 68–73
Levin G, Lieberman Z (2004) In-situ speech visualization in real-time interactive installation and performance. In: Non-Photorealistic Animation and Rendering: Proceedings of the 3rd international symposium on Non-photorealistic animation and rendering, vol 7. pp 7–14
Livshin AA, Rodet X (2004) Musical instrument identification in continuous recordings. In: Proceedings of DAFX
Mandanici M, Sapir S (2012) Disembodied voices: A kinect virtual choir conductor. http://www.smcnetwork.org/system/files/smc2012-174.pdf, last retrieved 20/09/2012
Morita H, Hashimoto S, Ohteru S (1991) A computer music system that follows a human conductor. Computer 24(7):44–53
Nakra T, Ivanov Y, Smaragdis P, Ault C (2009) The ubs virtual maestro: An interactive conducting system. NIME2009: 250–255
Ng K (2004) Music via motion: transdomain mapping of motion and sound for interactive performances. Proc IEEE 92(4):645–655
Padmavathi G, Shanmugapriya D, Kalaivani M (2010) Acoustic signal based feature extraction for vehicular classification. In: 3rd International conference on advanced computer theory and engineering (ICACTE), 2010 , vol 2. pp V2–11 –V2–14
Parton K, Edwards G (2009) Features of conductor gesture: towards a framework for analysis within interaction. In: The second international conference on music communication science, 3–4 December 2009. Sydney, Australia
Peng L, Gerhard D (2009) A wii-based gestural interface for computer-based conducting systems. In: Proceedings of the 2009 conference on new interfaces for musical expression
Qin Y, A study of wii/kinect controller as musical controllers. url=http://www.music.mcgill.ca/~ying/McGill/MUMT620/Wii-Kinect.pdf, last retrieved 20/09/2012
Rosa-Pujazón A, Barbancho I, Tardón LJ, Barbancho AM (2013) Conducting a virtual ensemble with a kinect device. In: SMAC 2013 - Stockholm music acoustics conference 2013. pp 284–291
Rosa-Pujazón A, Barbancho I, Tardón LJ, Barbancho AM (2013) Drum-hitting gesture recognition and prediction system using kinect. In:I Simposio Espaol de Entrenimiento Digital SEED’13. pp 108–118
Tardón LJ, Sammartino S, Barbancho I (2010) Design of an efficient music-speech discriminator. Acoust Soc Am
Theodoridis S, Koutroumbas K (2008) Pattern Recognition, Fourth Edition, 4th edn. Academic Press
Todoroff T, Leroy J, Picard-Limpens C (2011) Orchestra: Wireless sensor system for augmented performances & fusion with kinect. QPSR of the numediart research program 4(2)
Trail S, Dean M, Tavares T, Odowichuk G, Driessen P, Schloss W, Tzanetakis G (2012) Non-invasive sensing and gesture control for pitched percussion hyper-instruments using the kinect. In: Proceedings of the international conference on new interfaces for musical expression NIME’12
Wang C, Lai A (2011) Development of a mobile rhythm learning system based on digital game-based learning companion. Edutainment Technologies. Educ Game and Virtual Reality/Augmented Real Appl: 92–100
Acknowledgements
This work has been funded by the Junta de Andalucía under Project No. P11-TIC-7154 and by the Ministerio de Educación, Cultura y Deporte through the Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I-D + i 2008-2011, prorrogado por Acuerdo de Consejo de Ministros de 7 de octubre de 2011. The work has been done in the context of Campus de Excelencia Internacional Andalucía Tech, Universidad de Málaga.
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Herrero, G., Barbancho, I., Tardón, L.J. et al. Drumkit simulator from everyday desktop objects. Multimed Tools Appl 74, 10195–10213 (2015). https://doi.org/10.1007/s11042-014-2159-z
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DOI: https://doi.org/10.1007/s11042-014-2159-z