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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15785
Title: Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species
Authors: Ludeña Choez, Jimmy Diestin
Quispe Soncco, Raisa
Gallardo Antolín, Ascención
Keywords: acoustic analysis;analytic method;animal experiment;Article;audiometry;bird;controlled study;decomposition;hidden Markov model;kernel method;mel frequency cepstral coefficients;nonhuman;sound detection;species difference;speech analysis;support vector machine;task performance;vocalization;animal;classification
Issue Date: 2017
Publisher: Public Library of Science
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020903270&doi=10.1371%2fjournal.pone.0179403&partnerID=40&md5=2ffdb02b008e1d9dd3cd7db2ef308770
Abstract: Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. © 2017 Ludeña-Choez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15785
ISSN: 19326203
Appears in Collections:Artículos de investigación

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