On the Studies of Syllable Segmentation and Improving MFCCs for Automatic Birdsong Recognition

Chou, Chih-Hsun; Liu, Pang-Hsin; Cai, Bingjing

doi:10.1109/apscc.2008.6

Cited by 21 publications

(10 citation statements)

References 17 publications

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“…Chou, Liu and Cai [8] have proposed an enhanced syllable segmentation method based on Rabiner and Sambur endpoint detection method. This method is combined with a Melfrequency cepstral coefficients based feature vector to deal with two problems: syllable detection and birdsong section recognition.…”

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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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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“…En estos casos se requiere localizar los intervalos de tiempo que contienen vocalizaciones de animales en la grabación; este procedimiento se conoce como segmentación. Las vocalizaciones de algunos animales se han jerarquizado, por ejemplo en las aves, éstas se dividen en cuatro niveles: notas, sílabas, frases y cantos [42]; en la literatura se encuentran varios trabajos orientados a la clasificación de sílabas [4], [32], [35], [37], [42], [43]. Los métodos de segmentación más básicos están diseñados para detectar las regiones con mayor energía [44], sin embargo, en otros mé-todos más elaborados se busca detectar trayectorias características en las representaciones tiempo-frecuencia [37], [41], [45].…”

Section: Segmentaciónunclassified

Reconocimiento automatizado de señales bioacústicas: Una revisión de métodos y aplicaciones

2013

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ResumenDurante la última década se ha publicado una gran cantidad de estudios de investigación y aplicaciones sobre monitoreo bioacústico automatizado; no obstante, tales estudios están dispersos en la literatura de ingeniería y ciencias biológicas. En este artículo se presentan conceptos fundamentales sobre monitoreo bioacústico automatizado; se revisan, comparan y categorizan -en una taxonomía de técnicas de DSP/PR-las contribuciones de las investigaciones y las aplicaciones publicadas; se sugieren algunas direcciones para investigaciones futuras y se resaltan los retos y las oportunidades relacionados con la instalación de esta tecnología en Colombia.

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“…Mel-frequency scale is derived from the human perceptual system. Such systems in birds are not the same but exhibit similar characteristics; therefore, MFCCs have also been used in birdsong recognition [3,4]. The first 12 MFCCs, the log-energy and the so-called delta and delta-delta coefficients are obtained for each frame.…”

Section: Mel-cepstrum Representationmentioning

confidence: 99%

“…Several notes together in a regular pattern in a song constitute a syllable and, in turn, several syllables are a song phrase [1]. Previous studies [2][3][4] have shown that the sound-based recognition of bird species is suitable when considering syllables as elementary units. Raw measurements corresponding to those elementary units have to be represented in vector spaces where classification rules can afterwards be applied.…”

Section: Introductionmentioning

confidence: 99%

Feature and Dissimilarity Representations for the Sound-Based Recognition of Bird Species

Ruiz-Muñoz

Orozco-Alzate

Castellanos-Domínguez

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. Pattern recognition and digital signal processing techniques allow the design of automated systems for avian monitoring. They are a non-intrusive and cost-effective way to perform surveys of bird populations and assessments of biological diversity. In this study, a number of representation approaches for bird sounds are compared; namely, feature and dissimilarity representations. In order to take into account the nonstationary nature of the audio signals and to build robust dissimilarity representations, the application of the Earth Mover's Distance (EMD) to time-varying measurements is proposed. Measures of the leave-one-out 1-NN performance are used as comparison criteria. Results show that, overall, the Mel-ceptrum coefficients are the best alternative; specially when computed by frames and used in combination with EMD to generate dissimilarity representations.

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On the Studies of Syllable Segmentation and Improving MFCCs for Automatic Birdsong Recognition

Cited by 21 publications

References 17 publications

Feature set comparison for automatic bird species identification

Feature set comparison for automatic bird species identification

Reconocimiento automatizado de señales bioacústicas: Una revisión de métodos y aplicaciones

Feature and Dissimilarity Representations for the Sound-Based Recognition of Bird Species

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