Abstract
| Language | English |
|---|---|
| Pages | 457-475 |
| Number of pages | 19 |
| Journal | Journal of Intelligent Information Systems |
| Volume | 51 |
| Issue number | 3 |
| Early online date | 19 Aug 2017 |
| DOIs | |
| Publication status | Published - Dec 2018 |
| Externally published | Yes |
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Environmental sound recognition using short-time feature aggregation. / Roma, Gerard; Herrera, Perfecto; Nogueira, Waldo.
In: Journal of Intelligent Information Systems, Vol. 51, No. 3, 12.2018, p. 457-475.Research output: Contribution to journal › Article
TY - JOUR
T1 - Environmental sound recognition using short-time feature aggregation
AU - Roma, Gerard
AU - Herrera, Perfecto
AU - Nogueira, Waldo
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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.
KW - Audio databases
KW - Audio features
KW - Environmental sound recognition
KW - Event detection
KW - Pattern recognition
KW - Recurrence quantification analysis
U2 - 10.1007/s10844-017-0481-4
DO - 10.1007/s10844-017-0481-4
M3 - Article
VL - 51
SP - 457
EP - 475
JO - Journal of Intelligent Information Systems
T2 - Journal of Intelligent Information Systems
JF - Journal of Intelligent Information Systems
SN - 0925-9902
IS - 3
ER -