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Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News
This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. T. Theodorou, I. Mpoas, A. Lazaridis, N. Fakotakis, 'Data-Driven Audio Feature Space Clustering for Automatic Sound Recognition in Radio Broadcast News', International Journal on Artificial Intelligence Tools, Vol. 26 (2), April 2017, 1750005 (13 pages), DOI: 10.1142/S021821301750005. © The Author(s).In this paper we describe an automatic sound recognition scheme for radio broadcast news based on principal component clustering with respect to the discrimination ability of the principal components. Specifically, streams of broadcast news transmissions, labeled based on the audio event, are decomposed using a large set of audio descriptors and project into the principal component space. A data-driven algorithm clusters the relevance of the components. The component subspaces are used by sound type classifier. This methodology showed that the k-nearest neighbor and the artificial intelligent network provide good results. Also, this methodology showed that discarding unnecessary dimension works in favor on the outcome, as it hardly deteriorates the effectiveness of the algorithms.Peer reviewe
Acoustic Scene Classification
This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship
(EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council
Neonatal Seizure Detection using Convolutional Neural Networks
This study presents a novel end-to-end architecture that learns hierarchical
representations from raw EEG data using fully convolutional deep neural
networks for the task of neonatal seizure detection. The deep neural network
acts as both feature extractor and classifier, allowing for end-to-end
optimization of the seizure detector. The designed system is evaluated on a
large dataset of continuous unedited multi-channel neonatal EEG totaling 835
hours and comprising of 1389 seizures. The proposed deep architecture, with
sample-level filters, achieves an accuracy that is comparable to the
state-of-the-art SVM-based neonatal seizure detector, which operates on a set
of carefully designed hand-crafted features. The fully convolutional
architecture allows for the localization of EEG waveforms and patterns that
result in high seizure probabilities for further clinical examination.Comment: IEEE International Workshop on Machine Learning for Signal Processin
Supervised Classification of Baboon Vocalizations
International audienceThis paper addresses automatic classification of baboon vocalizations. We considered six classes of sounds emitted by "Papio papio" baboons, and report the results of supervised classification carried out with different signal representations (audio features), classifiers, combinations and settings. Results show that up to 94.1\% of correct recognition of pre-segmented elementary segments of vocalizations can be obtained using Mel-Frequency Cepstral Coefficients representation and Support Vector Machines classifiers. Results for other configurations are also presented and discussed, and a possible extension to the "Sound-spotting'' problem, i.e. online joint detection and classification of a vocalization from a continuous audio stream is illustrated and discussed
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