Abstract:  Many systems that provide automatic evaluation and feedback of electric guitar have been developed. However, they have a serious weakness, only “timing” and “pitch” are considered in evaluation. In fact, a wide range of factors are involved in the human evaluation. In order to solve this problem, previous studies proposed automatic evaluation methods. On the other hand, these are not possible to evaluate the sound using the special technique of electric guitar. In this study, we proposed a method for automatic proficiency estimation of vibrato in electric guitar. As the method, we extracted the acoustic features focusing on peaks of Mel fundamental frequency, number of peaks, width average, width variance, height average and height variance. We regressed the evaluation values using extracted acoustic features with a relevance vector machine; RVM. As the result, we were able to perform regression with a coefficient of determination 0.723. This result indicates extracted features are highly relevant to evaluation values by human and allows a tentative evaluation of the vibrato sound of the electric guitar.

Keywords: Electric Guitar, Vibrato, Proficiency Estimation, RVM, Mel Fundamental Frequency, Audio Signal Processing, Machine Learning, Music Information Processing, Music Education