Multi-pitch Detection and Voice Assignment for a Capella Recordings of Multiple Singers

Rodrigo Schramm, Andrew McLeod, Mark Steedman, Emmanouil Benetos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a multi-pitch detection and voice assignment method applied to audio recordings containing a cappella performances with multiple singers. A novel approach combining an acoustic model for multi-pitch detection and a music language model for voice separation and assignment is proposed. The acoustic model is a spectrogram factorization process based on Probabilistic Latent Component Analysis (PLCA), driven by a 6-dimensional dictionary with pre-learned spectral templates. The voice separation component is based on hidden Markov models that use musicological assumptions. By integrating the models, the system can detect multiple concurrent pitches in vocal music and assign each detected pitch to a specific voice corresponding to a voice type such as soprano, alto, tenor or bass (SATB). This work focuses on four-part compositions, and evaluations on recordings of Bach Chorales and Barbershop quartets show that our integrated approach achieves an F-measure of over 70% for frame-based multipitch detection and over 45% for four-voice assignment.
Original languageEnglish
Title of host publicationProceedings of the 18th International Society for Music Information Retrieval Conference
Place of PublicationSuzhou, China
Pages552-559
Number of pages8
ISBN (Electronic)978-981-11-5179-8
DOIs
Publication statusPublished - 27 Oct 2017
Event18th International Society for Music Information Retrieval Conference - National University of Singapore Research Institute (NUSRI), Suzhou, China
Duration: 23 Oct 201727 Oct 2017
https://ismir2017.smcnus.org/

Conference

Conference18th International Society for Music Information Retrieval Conference
Abbreviated titleISMIR 2017
CountryChina
CitySuzhou
Period23/10/1727/10/17
Internet address

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