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Appl. Sci. 2017, 7(12), 1285; https://doi.org/10.3390/app7121285

Automatic Transcription of Polyphonic Vocal Music

1
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
2
Departamento de Música, Universidade Federal do Rio Grande do Sul, Porto Alegre 90020, Brazil
3
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
This paper is an extended version of our paper published in R. Schramm, A. McLeod, M. Steedman, and E. Benetos. Multi-pitch detection and voice assignment for a cappella recordings of multiple singers. In 18th International Society for Music Information Retrieval Conference (ISMIR), pp. 552–559, 2017.
These authors contributed equally to this work.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Meinard Müller
Received: 31 October 2017 / Revised: 1 December 2017 / Accepted: 4 December 2017 / Published: 11 December 2017
(This article belongs to the Special Issue Sound and Music Computing)
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Abstract

This paper presents a method for automatic music transcription applied to audio recordings of a cappella performances with multiple singers. We propose a system for multi-pitch detection and voice assignment that integrates an acoustic and a music language model. The acoustic model performs spectrogram decomposition, extending probabilistic latent component analysis (PLCA) using a six-dimensional dictionary with pre-extracted log-spectral templates. The music language model performs voice separation and assignment using hidden Markov models that apply musicological assumptions. By integrating the two models, the system is able to detect multiple concurrent pitches in polyphonic vocal music and assign each detected pitch to a specific voice type such as soprano, alto, tenor or bass (SATB). We compare our system against multiple baselines, achieving state-of-the-art results for both multi-pitch detection and voice assignment on a dataset of Bach chorales and another of barbershop quartets. We also present an additional evaluation of our system using varied pitch tolerance levels to investigate its performance at 20-cent pitch resolution. View Full-Text
Keywords: automatic music transcription; multi-pitch detection; voice assignment; music signal analysis; music language models; polyphonic vocal music; music information retrieval automatic music transcription; multi-pitch detection; voice assignment; music signal analysis; music language models; polyphonic vocal music; music information retrieval
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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McLeod, A.; Schramm, R.; Steedman, M.; Benetos, E. Automatic Transcription of Polyphonic Vocal Music. Appl. Sci. 2017, 7, 1285.

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