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A Hierarchical Latent Mixture Model for Polyphonic Music Analysis

O’Brien, Cian and Plumbley, Mark D (2018) A Hierarchical Latent Mixture Model for Polyphonic Music Analysis In: 2018 26th European Signal Processing Conference (EUSIPCO), 3 - 7 September 2018, Rome. Italy.

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Abstract

Polyphonic music transcription is a challenging problem, requiring the identification of a collection of latent pitches which can explain an observed music signal. Many state-of-the-art methods are based on the Non-negative Matrix Factorization (NMF) framework, which itself can be cast as a latent variable model. However, the basic NMF algorithm fails to consider many important aspects of music signals such as lowrank or hierarchical structure and temporal continuity. In this work we propose a probabilistic model to address some of the shortcomings of NMF. Probabilistic Latent Component Analysis (PLCA) provides a probabilistic interpretation of NMF and has been widely applied to problems in audio signal processing. Based on PLCA, we propose an algorithm which represents signals using a collection of low-rank dictionaries built from a base pitch dictionary. This allows each dictionary to specialize to a given chord or interval template which will be used to represent collections of similar frames. Experiments on a standard music transcription data set show that our method can successfully decompose signals into a hierarchical and smooth structure, improving the quality of the transcription.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
O’Brien, Cian
Plumbley, Mark Dm.plumbley@surrey.ac.uk
Date : 3 December 2018
Funders : European Union’s Seventh Framework Programme
DOI : 10.23919/EUSIPCO.2018.8553244
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Related URLs :
Depositing User : Melanie Hughes
Date Deposited : 22 Aug 2018 12:39
Last Modified : 11 Dec 2018 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/849062

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