Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning
Open accessAbstract
Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and "unsupervised", meaning it requires no manual data labelling, yet it can improve performance on "supervised" tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased performance is not discernible. We study the interaction between dataset characteristics and choice of feature representation through further empirical analysis.
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Mel-frequency cepstral coefficients (MFCCs) became the workhorse representation for animal vocalizations, distilling the spectral envelope of calls into a form that machine-learning algorithms can classify with impressive accuracy.
“Mel-frequency cepstral coefficients (MFCCs) became the workhorse representation for animal vocalizations, distilling the spectral envelope of calls into a form that machine-learning algorithms can classify with impressive accuracy”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Stowell and Plumbley 2014
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- Authors
- Stowell, D., & Plumbley, M. D.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Stowell, Dan
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2014-07-17
- Volume
- 2
- Pages
- e488
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC4106198
- OpenAlex
- W2069943693
- Cited by
- 295
- References
- 42
- Citations by year
- 2026: 17 · 2025: 27 · 2024: 20 · 2023: 18 · 2022: 27 · 2021: 29 · 2020: 29 · 2019: 27 · 2018: 30 · 2017: 28 · 2016: 20 · 2015: 20 · 2014: 3
- FWCI
- 30.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PeerJ (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- Research portal (Tilburg University) (publishedVersion, other-oa)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/L020505/1, EP/G007144/1)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- bird species classification, bird vocalizations, unsupervised feature learning, Mel-frequency cepstral coefficients, Mel spectrum, spectrotemporal receptive fields, random forest classification
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