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Classification of general audio data for content-based retrieval
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Multimedia Tools and Applications
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FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
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Foundations and Trends in Signal Processing
Content based audio classification: a neural network approach
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Content-Based Classification and Segmentation of Mixed-Type Audio by Using MPEG-7 Features
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Introduction to Machine Learning
Introduction to Machine Learning
Audio signal representations for indexing in the transform domain
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Segmentation, indexing, and retrieval for environmental and natural sounds
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IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a ''Divide and Conquer'' approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases.