TY - GEN
T1 - AGREEMENT AMONG HUMAN AND AUTOMATED TRANSCRIPTIONS OF GLOBAL SONGS
AU - Ozaki, Yuto
AU - McBride, John
AU - Benetos, Emmanouil
AU - Pfordresher, Peter Q.
AU - Six, Joren
AU - Tierney, Adam T.
AU - Proutskova, Polina
AU - Sakai, Emi
AU - Kondo, Haruka
AU - Fukatsu, Haruno
AU - Fujii, Shinya
AU - Savage, Patrick E.
N1 - Publisher Copyright:
© 2021 Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - Cross-cultural musical analysis requires standardized symbolic representation of sounds such as score notation. However, transcription into notation is usually conducted manually by ear, which is time-consuming and subjective. Our aim is to evaluate the reliability of existing methods for transcribing songs from diverse societies. We had 3 experts independently transcribe a sample of 32 excerpts of traditional monophonic songs from around the world (half a cappella, half with instrumental accompaniment). 16 songs also had pre-existing transcriptions created by 3 different experts. We compared these human transcriptions against one another and against 10 automatic music transcription algorithms. We found that human transcriptions can be sufficiently reliable (~90% agreement, κ ~.7), but current automated methods are not (<60% agreement, κ <.4). No automated method clearly outperformed others, in contrast to our predictions. These results suggest that improving automated methods for cross-cultural music transcription is critical for diversifying MIR.
AB - Cross-cultural musical analysis requires standardized symbolic representation of sounds such as score notation. However, transcription into notation is usually conducted manually by ear, which is time-consuming and subjective. Our aim is to evaluate the reliability of existing methods for transcribing songs from diverse societies. We had 3 experts independently transcribe a sample of 32 excerpts of traditional monophonic songs from around the world (half a cappella, half with instrumental accompaniment). 16 songs also had pre-existing transcriptions created by 3 different experts. We compared these human transcriptions against one another and against 10 automatic music transcription algorithms. We found that human transcriptions can be sufficiently reliable (~90% agreement, κ ~.7), but current automated methods are not (<60% agreement, κ <.4). No automated method clearly outperformed others, in contrast to our predictions. These results suggest that improving automated methods for cross-cultural music transcription is critical for diversifying MIR.
UR - https://www.scopus.com/pages/publications/85180131584
M3 - Conference contribution
AN - SCOPUS:85180131584
T3 - Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021
SP - 500
EP - 508
BT - ISMIR 2021 - The International Society For Music Information Retrieval Conference, Proceedings
PB - International Society for Music Information Retrieval
T2 - 22nd International Society for Music Information Retrieval Conference, ISMIR 2021
Y2 - 7 November 2021 through 12 November 2021
ER -