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Environmental sound recognition using short-time feature aggregation
Title | Environmental sound recognition using short-time feature aggregation |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Roma, G. , Herrera P. , & Nogueira W. |
Journal Title | Journal of Intelligent Information Systems |
Pages | 1-19 |
Journal Date | 08/2017 |
ISSN | 1573-7675 |
Final publication | 10.1007/s10844-017-0481-4 |
Original Publication | http://rdcu.be/u9f7 |
Additional material:
Recognition of environmental sound is usually based on two main architectures, depending on whether the model is trained with frame-level features or with aggregated descriptions of acoustic scenes or events. The former architecture is appropriate for applications where target categories are known in advance, while the later affords a less supervised approach. In this paper, we propose a framework for environmental sound recognition based on blind segmentation and feature aggregation. We describe a new set of descriptors, based on Recurrence Quantification Analysis (RQA), which can be extracted from the similarity matrix of a time series of audio descriptors. We analyze their usefulness for recognition of acoustic scenes and events in addition to standard feature aggregation. Our results show the potential of non-linear time series analysis techniques for dealing with environmental sounds.