SDIRASApr 15, 2020

Musical Features for Automatic Music Transcription Evaluation

arXiv:2004.07171v1
AI Analysis

This work addresses the need for better evaluation methods in music information retrieval, but it is incremental as it builds on prior metrics without proposing a new paradigm.

The paper tackles the problem of evaluating automatic piano music transcription by introducing musical features to assess the perceptual validity of existing metrics, finding that current metrics do not fully align with human perception.

This technical report gives a detailed, formal description of the features introduced in the paper: Adrien Ycart, Lele Liu, Emmanouil Benetos and Marcus T. Pearce. "Investigating the Perceptual Validity of Evaluation Metrics for Automatic Piano Music Transcription", Transactions of the International Society for Music Information Retrieval (TISMIR), Accepted, 2020.

Foundations

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