10.5281/zenodo.1120334
https://zenodo.org/records/1120334
oai:zenodo.org:1120334
Bosch, Juan J.
Juan J.
Bosch
0000-0003-4221-3517
Universitat Pompeu Fabra, Barcelona
From heuristics-based to data-driven audio melody extraction
Zenodo
2017
Melody Extraction
Automatic
MIR
Music
Retrieval
Symphonic
Instrument
Agreement
Tonality
Timbre
Stereo
Source-filter
Separation
NMF
Visualisation
Evaluation
Dataset
Contour
Salience
Pitch
Supervised
Gómez, Emilia
Emilia
Gómez
Universitat Pompeu Fabra, Barcelona
2017-06-27
eng
Thesis
http://mtg.upf.edu/node/3737
10.5281/zenodo.1120333
https://zenodo.org/communities/mir
https://zenodo.org/communities/mdm-dtic-upf
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
Abstract
The identification of the melody from a music recording is a relatively easy task for humans, but very challenging for computational systems. This task is known as "audio melody extraction", more formally defined as the automatic estimation of the pitch sequence of the melody directly from the audio signal of a polyphonic music recording. This thesis investigates the benefits of exploiting knowledge automatically derived from data for audio melody extraction, by combining digital signal processing and machine learning methods. We extend the scope of melody extraction research by working with a varied dataset and multiple definitions of melody. We first present an overview of the state of the art, and perform an evaluation focused on a novel symphonic music dataset. We then propose melody extraction methods based on a source-filter model and pitch contour characterisation and evaluate them on a wide range of music genres. Finally, we explore novel timbre, tonal and spatial features for contour characterisation, and propose a method for estimating multiple melodic lines. The combination of supervised and unsupervised approaches leads to advancements on melody extraction and shows a promising path for future research and applications.
Datasets:
The symphonic music dataset proposed in this thesis (Orchset) is available at:
https://zenodo.org/record/1289786#.XnNV15P0mL8
Orchset is intended to be used as a dataset for the development and evaluation of melody extraction algorithms. This collection contains 64 audio excerpts focused on symphonic music. with their corresponding annotation of the melody.
Code:
The source code of the melody extraction algorithms proposed in this thesis is available at:
https://github.com/juanjobosch/SourceFilterContoursMelody