Bird Species Recognition by Wavelet Transformation of a Section of Birdsong

Chou, Chih-Hsun; Liu, Pang-Hsin

doi:10.1109/uic-atc.2009.85

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…Bastas et al (2012) observed that Discrete Wavelet Transformation (DWT)based features outperformed MFCC. Despite the opportunities that wavelets provide, relatively few publications have so far used them for birdsong analysis (Turunen et al 2006, Selin et al 2007, Chou and Liu 2009, Zhang and Li 2015.…”

Section: Feature Choice and Extractionmentioning

confidence: 99%

Automated birdsong recognition in complex acoustic environments: a review

Priyadarshani

Marsland

Castro

2018

Journal of Avian Biology

209

216

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Conservationists are increasingly using autonomous acoustic recorders to determine the presence/absence and the abundance of bird species. Unlike humans, these recorders can be left in the field for extensive periods of time in any habitat. Although data acquisition is automated, manual processing of recordings is labour intensive, tedious, and prone to bias due to observer variations. Hence automated birdsong recognition is an efficient alternative. However, only few ecologists and conservationists utilise the existing birdsong recognisers to process unattended field recordings because the software calibration time is exceptionally high and requires considerable knowledge in signal processing and underlying systems, making the tools less user‐friendly. Even allowing for these difficulties, getting accurate results is exceedingly hard. In this review we examine the state‐of‐the‐art, summarising and discussing the methods currently available for each of the essential parts of a birdsong recogniser, and also available software. The key reasons behind poor automated recognition are that field recordings are very noisy, calls from birds that are a long way from the recorder can be faint or corrupted, and there are overlapping calls from many different birds. In addition, there can be large numbers of different species calling in one recording, and therefore the method has to scale to large numbers of species, or at least avoid misclassifying another species as one of particular interest. We found that these areas of importance, particularly the question of noise reduction, are amongst the least researched. In cases where accurate recognition of individual species is essential, such as in conservation work, we suggest that specialised (species‐specific) methods of passive acoustic monitoring are required. We also believe that it is important that comparable measures, and datasets, are used to enable methods to be compared.

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Section: Feature Choice and Extractionmentioning

confidence: 99%

Automated birdsong recognition in complex acoustic environments: a review

Priyadarshani

Marsland

Castro

2018

Journal of Avian Biology

209

216

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“…Chou and Liu [9] have used a wavelet transformation to transform sections of the bird songs. Then, the first five order Mel-frequency cepstral coefficients are computed, and the Melfrequency cepstral coefficients of the same order are aligned.…”

Section: Related Workmentioning

confidence: 99%

Feature set comparison for automatic bird species identification

Lopes

Silla

Koerich

et al. 2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-This paper deals with the automated bird species identification problem, in which it is necessary to identify the species of a bird from its audio recorded song. This is a clever way to monitor biodiversity in ecosystems, since it is an indirect non-invasive way of evaluation. Different features sets which summarize in different aspects the audio properties of the audio signal are evaluated in this paper together with machine learning algorithms, such as probabilistic, instance-based, decision trees, neural networks and support vector machines. Experiments are conducted in a dataset of recorded songs of three bird species. The experimental results compare the performance of the features sets and different classifiers showing that it is possible to obtain very promising results in the automated bird species identification problem.

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“…In the experiments they obtain the best classification accuracy of 84% for 28 bird species. Chou and Liu [8] use a wavelet transformation to transform sections of the bird songs. Then the first five order MFCCs are computed, and same order MFCC are aligned.…”

Section: B Related Workmentioning

confidence: 99%

Automatic Bird Species Identification for Large Number of Species

Lopes

Gioppo

Higushi

et al. 2011

2011 IEEE International Symposium on Multimedia

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In this paper we focus on the automatic identification of bird species from their audio recorded song. Bird monitoring is important to perform several tasks, such as to evaluate the quality of their living environment or to monitor dangerous situations to planes caused by birds near airports. We deal with the bird species identification problem using signal processing and machine learning techniques. First, features are extracted from the bird recorded songs using specific audio treatment; next the problem is performed according to a classical machine learning scenario, where a labeled database of previously known bird songs are employed to create a decision procedure that is used to predict the species of a new bird song. Experiments are conducted in a dataset of recorded songs of bird species which appear in a specific region. The experimental results compare the performance obtained in different situations, encompassing the complete audio signals, as recorded in the field, and short audio segments (pulses) obtained from the signals by a split procedure. The influence of the number of classes (bird species) in the identification accuracy is also evaluated.

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Bird Species Recognition by Wavelet Transformation of a Section of Birdsong

Cited by 14 publications

References 11 publications

Automated birdsong recognition in complex acoustic environments: a review

Automated birdsong recognition in complex acoustic environments: a review

Feature set comparison for automatic bird species identification

Automatic Bird Species Identification for Large Number of Species

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