University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Audio-only bird classification using unsupervised feature learning

Stowell, D and Plumbley, MD (2014) Audio-only bird classification using unsupervised feature learning In: Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15-18, 2014.

[img]
Preview
Text
StowellEtAl14-clef_published_authorcopyright.pdf - ["content_typename_Published version (Publisher's proof or final PDF)" not defined]
Available under License : See the attached licence file.

Download (861kB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

We describe our method for automatic bird species classification, which uses raw audio without segmentation and without using any auxiliary metadata. It successfully classifies among 501 bird categories, and was by far the highest scoring audio-only bird recognition algorithm submitted to BirdCLEF 2014. Our method uses unsupervised feature learning, a technique which learns regularities in spectro-temporal content without reference to the training labels, which helps a classifier to generalise to further content of the same type. Our strongest submission uses two layers of feature learning to capture regularities at two different time scales.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Stowell, DUNSPECIFIEDUNSPECIFIED
Plumbley, MDUNSPECIFIEDUNSPECIFIED
Date : September 2014
Contributors :
ContributionNameEmailORCID
EditorCappellato, LUNSPECIFIEDUNSPECIFIED
EditorFerro, NUNSPECIFIEDUNSPECIFIED
EditorHalvey, MUNSPECIFIEDUNSPECIFIED
EditorKraaij, WUNSPECIFIEDUNSPECIFIED
Related URLs :
Additional Information : Copyright 2014 The Authors.
Depositing User : Symplectic Elements
Date Deposited : 22 Apr 2015 14:45
Last Modified : 18 Jun 2015 13:33
URI: http://epubs.surrey.ac.uk/id/eprint/807467

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800