Bird species recognition using support vector machines
EURASIP Journal on Applied Signal Processing
Frog classification using machine learning techniques
Expert Systems with Applications: An International Journal
Improvement to speech-music discrimination using sinusoidal model based features
Multimedia Tools and Applications
Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring
Pattern Recognition Letters
Automatic birdsong recognition with MFCC based syllable feature extraction
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
Automatic recognition of frog calls using a multi-stage average spectrum
Computers & Mathematics with Applications
Analysing environmental acoustic data through collaboration and automation
Future Generation Computer Systems
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This paper is related to the development of signal processing techniques for automatic recognition of bird species. Three different parametric representations are compared. The first representation is based on sinusoidal modeling which has been earlier found useful for highly tonal bird sounds. Mel-cepstrum parameters are used since they have been found very useful in the parallel problem of speech recognition. Finally, a vector of various descriptive features is tested because such models are popular in audio classification applications, and bird song is almost like music. We briefly introduce the methods and evaluate their performance in the classification and recognition of both individual syllables and song fragments of 14 common North-European Passerine bird species