Bosch, J.J., Marxer, R. and Gómez, E. (2016) Evaluation and combination of pitch estimation methods for melody extraction in symphonic classical music. Journal of New Music Research. pp. 1-17. ISSN 0929-8215
Abstract
The extraction of pitch information is arguably one of the most important tasks in automatic music description systems. However, previous research and evaluation datasets dealing with pitch estimation focused on relatively limited kinds of musical data. This work aims to broaden this scope by addressing symphonic western classical music recordings, focusing on pitch estimation for melody extraction. This material is characterised by a high number of overlapping sources, and by the fact that the melody may be played by different instrumental sections, often alternating within an excerpt. We evaluate the performance of eleven state-of-the-art pitch salience functions, multipitch estimation and melody extraction algorithms when determining the sequence of pitches corresponding to the main melody in a varied set of pieces. An important contribution of the present study is the proposed evaluation framework, including the annotation methodology, generated dataset and evaluation metrics. The results show that the assumptions made by certain methods hold better than others when dealing with this type of music signals, leading to a better performance. Additionally, we propose a simple method for combining the output of several algorithms, with promising results.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2016 Taylor & Francis. This is an author produced version of a paper subsequently published in Journal of New Music Research. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Aug 2016 09:09 |
Last Modified: | 23 Nov 2017 01:38 |
Published Version: | http://dx.doi.org/10.1080/09298215.2016.1182191 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/09298215.2016.1182191 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103396 |