Environmental sound recognition using short-time feature aggregation

TitleEnvironmental sound recognition using short-time feature aggregation
Publication TypeJournal Article
Year of Publication2017
AuthorsRoma, G., Herrera P., & Nogueira W.
Journal TitleJournal of Intelligent Information Systems
Pages1-19
Journal Date08/2017
ISSN1573-7675
Keywordsaudio analysis, audio background recognition, audio event recognition, Audio features, environmental sound recognition, recurrence quantification analysis, sound effects
Final publication10.1007/s10844-017-0481-4
Original Publicationhttp://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.
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