publication . Other literature type . Preprint . Part of book or chapter of book . Conference object . 2019

A Convolutional Approach to Melody Line Identification in Symbolic Scores

Simonetta, Federico; Cancino-Chacón, Carlos; Ntalampiras, Stavros; Widmer, Gerhard;
Open Access
  • Published: 04 Nov 2019
  • Publisher: Zenodo
  • Country: Italy
Abstract
In many musical traditions, the melody line is of primary significance in a piece. Human listeners can readily distinguish melodies from accompaniment; however, making this distinction given only the written score -- i.e. without listening to the music performed -- can be a difficult task. Solving this task is of great importance for both Music Information Retrieval and musicological applications. In this paper, we propose an automated approach to identifying the most salient melody line in a symbolic score. The backbone of the method consists of a convolutional neural network (CNN) estimating the probability that each note in the score (more precisely: each pix...
Subjects
free text keywords: Computer Science - Sound, Computer Science - Information Retrieval, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, Settore INF/01 - Informatica, Music Information Retrieval, Convolutional Neural Network, Melody Identification, Symbolic scores, MIR
Funded by
EC| Con Espressione
Project
Con Espressione
Getting at the Heart of Things: Towards Expressivity-aware Computer Systems in Music
  • Funder: European Commission (EC)
  • Project Code: 670035
  • Funding stream: H2020 | ERC | ERC-ADG
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publication . Other literature type . Preprint . Part of book or chapter of book . Conference object . 2019

A Convolutional Approach to Melody Line Identification in Symbolic Scores

Simonetta, Federico; Cancino-Chacón, Carlos; Ntalampiras, Stavros; Widmer, Gerhard;