Ten lectures on wavelets
Feature Extraction From Wavelet Coefficients for Pattern Recognition Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Self-Organizing Maps
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets
Birdsong recognition using backpropagation and multivariatestatistics
IEEE Transactions on Signal Processing
Bird species recognition using support vector machines
EURASIP Journal on Applied Signal Processing
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This paper presents a novel method to recognize inharmonic and transient bird sounds efficiently. The recognition algorithm consists of feature extraction using wavelet decomposition and recognition using either supervised or unsupervised classifier. The proposed method was tested on sounds of eight bird species of which five species have inharmonic sounds and three reference species have harmonic sounds. Inharmonic sounds are not well matched to the conventional spectral analysis methods, because the spectral domain does not include any visible trajectories that computer can track and identify. Thus, the wavelet analysis was selected due to its ability to preserve both frequency and temporal information, and its ability to analyze signals which contain discontinuities and sharp spikes. The shift invariant feature vectors calculated from the wavelet coefficients were used as inputs of two neural networks: the unsupervised self-organizing map (SOM) and the supervised multilayer perceptron (MLP). The results were encouraging: the SOM network recognized 78% and the MLP network 96% of the test sounds correctly.