Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An automated acoustic systemto monitor and classify birds
EURASIP Journal on Applied Signal Processing
Constructing Modulation Frequency Domain-Based Features for Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Parametric Representations of Bird Sounds for Automatic Species Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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In this study, an automatic birdsong recognition system based on syllable features was developed. In this system, after syllable segmentation, three syllable features, namely mean, QI and QE, were computed from the MFCCs of each syllable aims at capturing variations in time as well as amplitude transitions of the MFCC sequences. With the advantages of the fuzzy c-mean (FCM) clustering algorithm and the linear discriminant analysis (LDA), the presented feature vector was used to construct an automatic birdsong recognition system applied to a birdsong database with 420 bird species.