Environmental sound recognition using short-time feature aggregation

Gerard Roma, Perfecto Herrera, Waldo Nogueira

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)457-475
Number of pages19
JournalJournal of Intelligent Information Systems
Issue number3
Early online date19 Aug 2017
Publication statusPublished - Dec 2018
Externally publishedYes


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