DocumentCode :
702335
Title :
Automatic identification of bird species: A comparison between kNN and SOM classifiers
Author :
Kaminska, Dorota ; Gmerek, Artur
Author_Institution :
Inst. of Mechatron. & Inf. Syst., Tech. Univ. of Lodz, Lodz, Poland
fYear :
2012
fDate :
27-29 Sept. 2012
Firstpage :
77
Lastpage :
82
Abstract :
This paper presents a system for automatic bird identification, which uses audio input. The experiments have been conducted on three groups of birds, which were created basing finishing on classification, the system is fully automated. The main problem in automatic bird recognition (ABR) is the choice of proper features and classifiers. Identification has been made using two classifiers-kNN (k Nearest Neighbor) and SOM (Self Organizing Maps). System has been tested using data extracted from natural environment.
Keywords :
audio recording; audio signal processing; pattern classification; self-organising feature maps; ABR; SOM classifiers; audio input; automatic bird identification; automatic bird recognition; bird species; data extraction; k nearest neighbor; kNN classifiers; natural environment; self organizing maps; Accuracy; Birds; Correlation; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Training; HMM; SOM; birds; identification; kNN; recognition; self organizing maps;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
New Trends in Audio & Video and Signal Processing: Algorithms, Architectures, Arrangements, and Applications (NTAV/SPA), 2012 Joint Conference
Conference_Location :
Lodz
Print_ISBN :
978-8-3728-3502-4
Type :
conf
Filename :
7085515
Link To Document :
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