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Content-based audio classification using collective network of binary classifiers

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Název: Content-based audio classification using collective network of binary classifiers
Autoři: Mäkinen, T., Kiranyaz, S., Gabbouj, M.
Rok vydání: 2011
Sbírka: The Hong Kong University of Science and Technology: HKUST Institutional Repository
Témata: Audio content - based classification, Evolutionary neural networks, Multilayer perceptron, Particle swarm optimization
Popis: In this paper, a novel collective network of binary classifiers (CNBC) framework is presented for content-based audio classification. The topic has been studied in several publications before, but in many cases the number of different classification categories is quite limited and needed to be fixed a priori. We focus our efforts to increase both the classification accuracy and the number of classes, as well as to create a scalable network design, which allows introducing new audio classes incrementally. The approach is based on dividing a major classification problem into several networks of binary classifiers (NBCs), where each NBC adapts its internal topology according to the classification problem at hand, by using evolutionary Artificial Neural Networks (ANNs). In the current work, feed-forward ANNs, or the so-called Multilayer Perceptrons (MLPs), are evolved within an architecture space, where a stochastic optimization is applied to seek for the optimal classifier configuration and parameters. The performance evaluations of the proposed framework over an 8-class benchmark audio database demonstrate its scalability and notable potential, as classification error rates of less than 9% are achieved. © 2011 IEEE.
Druh dokumentu: conference object
Jazyk: English
Relation: https://doi.org/10.1109/EAIS.2011.5945911
DOI: 10.1109/EAIS.2011.5945911
Dostupnost: http://repository.hkust.edu.hk/ir/Record/1783.1-53770
https://doi.org/10.1109/EAIS.2011.5945911
http://www.scopus.com/record/display.url?eid=2-s2.0-80051479510&origin=inward
Přístupové číslo: edsbas.D7EE55A6
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  – Url: http://repository.hkust.edu.hk/ir/Record/1783.1-53770#
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  Data: Content-based audio classification using collective network of binary classifiers
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  Data: <searchLink fieldCode="AR" term="%22Mäkinen%2C+T%2E%22">Mäkinen, T.</searchLink><br /><searchLink fieldCode="AR" term="%22Kiranyaz%2C+S%2E%22">Kiranyaz, S.</searchLink><br /><searchLink fieldCode="AR" term="%22Gabbouj%2C+M%2E%22">Gabbouj, M.</searchLink>
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  Data: 2011
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  Data: The Hong Kong University of Science and Technology: HKUST Institutional Repository
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  Data: <searchLink fieldCode="DE" term="%22Audio+content+-+based+classification%22">Audio content - based classification</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+neural+networks%22">Evolutionary neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Multilayer+perceptron%22">Multilayer perceptron</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink>
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  Data: In this paper, a novel collective network of binary classifiers (CNBC) framework is presented for content-based audio classification. The topic has been studied in several publications before, but in many cases the number of different classification categories is quite limited and needed to be fixed a priori. We focus our efforts to increase both the classification accuracy and the number of classes, as well as to create a scalable network design, which allows introducing new audio classes incrementally. The approach is based on dividing a major classification problem into several networks of binary classifiers (NBCs), where each NBC adapts its internal topology according to the classification problem at hand, by using evolutionary Artificial Neural Networks (ANNs). In the current work, feed-forward ANNs, or the so-called Multilayer Perceptrons (MLPs), are evolved within an architecture space, where a stochastic optimization is applied to seek for the optimal classifier configuration and parameters. The performance evaluations of the proposed framework over an 8-class benchmark audio database demonstrate its scalability and notable potential, as classification error rates of less than 9% are achieved. © 2011 IEEE.
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