Humanities and engineering perspectives on music transcription

Abstract

Music transcription is a process of creating a notation of musical sounds. It has been used as a basis for the analysis of music from a wide variety of cultures. Recent decades have seen an increasing amount of engineering research within the field of Music Information Retrieval that aims at automatically obtaining music transcriptions in Western staff notation. However, such approaches are not widely applied in research in ethnomusicology. This article aims to bridge interdisciplinary gaps by identifying aspects of proximity and divergence between the two fields. As part of our study, we collected manual transcriptions of traditional dance tune recordings by eighteen transcribers. Our method employs a combination of expert and computational evaluation of these transcriptions. This enables us to investigate the limitations of automatic music transcription (AMT) methods and computational transcription metrics that have been proposed for their evaluation. Based on these findings, we discuss promising avenues to make AMT more useful for studies in the Humanities. These are, first, assessing the quality of a transcription based on an analytic purpose; secondly, developing AMT approaches that are able to learn conventions concerning the transcription of a specific style; thirdly, a focus on novice transcribers as users of AMT systems; and, finally, considering target notation systems different from Western staff notation.

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

“…In 2022, Holzapfelet al [71] contrasted two areas, namely the examination of these transcriptions by experts and computers. Initially, the transcription of eighteen transcribers was collected and evaluated with computational transcription through music transcription (AMT) methods.…”

A Study on Automatic Composition for Chinese Wind Piano in the Framework of Autoregressive Language Modeling

“…GRUs is a recurrent neural network layer, and the model uses a bidirectional recurrent neural network, whose purpose is to extract features through the upper recurrent neural network. Neural network, whose purpose is to make predictions of notes and related musical information from the information extracted in the previous layer of the convolutional network [19].…”