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Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy
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  • Vipul Arora ,
  • Suraj Jaiswal ,
  • Akshay Raina ,
  • Sumit Kumar
Vipul Arora
IIT Kanpur

Corresponding Author:[email protected]

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Suraj Jaiswal
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Akshay Raina
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Sumit Kumar
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The growing popularity of computer-assisted pedagogy and audio analysis methods based on machine learning stimulates research into developing tools for music pedagogy. Despite the proliferation of commercial tools for teaching music, a lack of systematic research on computer-assisted music pedagogy exists. This paper describes pedagogical aids for music education. Novel methods are proposed for detecting singing mistakes by comparing teacher’s and learner’s audio. These methods consist of a CNN-based model for comparing pitch and amplitude contours and a CRNN-based model for comparing spectrograms. A new evaluation method is proposed to compare the efficacy of mistake detection systems. Experiments indicate that the proposed learning-based methods are superior to the rule-based baseline. A systematic study of errors and a cross-teacher study reveal insights into music pedagogy that can be utilized when deploying the proposed tool. In addition, a new dataset of teacher and learner audio recordings, annotated for singing mistakes, is presented.