Pattern analysis based acoustic signal for trigger detection
Keywords:
music information retrieval, acoustic signals processing, musical genres, data-setAbstract
This work analyzes in detail an international public database of musical audios about the acoustic features present in the existing samples. A total of one thousand audios classified in ten musical genres were analyzed in relation to their acoustic characteristics to look for patterns in the triggers present in the samples of each musical genre and the amount of these for application in subsequent studies of brain activations generated by listening to these songs. The results show that among the ten musical genres present in the audio database, two of these (Disco and Metal) do not have enough triggers for such an application.
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Ferreira, L. A., Ribeiro, E. and Thomaz, C. E. (2019) “A cluster analysis of benchmark acoustic features on Brazilian music”, In: SBCM, pp.1–3.
Greenberg, D. M., et al. (2015) “Musical Preferences are linked to cognitive styles”, In: PLOS ONE, pp.1–22.
Istók, E., et al. (2013) “’I love Rock n’ Roll’ – Music genre preference modulates brain response to music”, In: Biological Psychology, v. 92, pp.142–151.
Lartillot, O. (2019) “MIRtoolbox 1.7.2 user’s manual”, Aalborg: Department of architecture, design e media technology.
Lerch, A. (2012) An introduction to audio content analysis, New Jersey: IEEE, 1st edition.
Kness, P. and Schedl, M. (2016), Music similarity and retrieval: an introduction to audio and web-based strategies, Heidelberg: Springer, 1st edition.
Mehl, M. R., Pennebaker, J. W. (2003) “The Sounds of Social Life: A Psychometric Analysis of Students’ Daily Social Environments and Natural Conversations”, In: Journal of Personality and Social Psychology, v. 84, n. 4, p.857–870.
Peretz, I., Zatorre, R. J., (2005) “Brain Organization for Music Processing”, In: Annual Reviewe of Psychology, v. 56, n. 1, p.89–114.
Poikonen, H., et al. (2016) “Event-related brain responses while listening to entire pieces of music”, In: Neuroscience, v. 312, p.58–73.
Rentfrow, P. J., Gosling, S. D. (2003) “The do re mi’s of everyday life: The structure and personality correlates to music preference”, In: Journal of Personality and Social Psychology, v. 84, n. 6, p.1236–1256.
Ribeiro, E. (2017) “UM ESTUDO SOBRE PREDIÇÃO DE MUSICALIDADE POR MEIO DA ANÁLISE DE SINAIS DE EEG”, In: Dissertação de Mestrado em Engenharia Elétria, FEI, São Bernardo do Campo, Brasil.
Ribeiro, E., Thomaz, C. E., (2019) “Whole brain EEG analysis of musicianship”, In: Music Perception, v. 37, pp. 42–56.
Ribeiro, E. (2020) “ANÁLISE E RECONHECIMENTO DE PADRÕES COGNITIVOS EM ESCUTAS MUSICAIS E SONOROS EM ÁUDIO”, In: Tese de Doutorado em Engenharia Elétrica, FEI, São Bernardo do Campo, Brasil.
Soleymani, M., et al. (2015) “Content-Based music recommendation using underlying music preference structure”, In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6.
Tzanetakis, G., Cook, P. (2002) “Musical genre classification on audio signals”, In: IEEE Transactions on Speech and Audio Processing, v. 10, n. 5, p.293–302.
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