From heuristics-based to data-driven audio melody extraction

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