Audio Content Analysis for Unobtrusive Event Detection in Smart Homes
| dc.cclicence | CC-BY-NC-ND | en |
| dc.contributor.author | Vafeiadis, Anastasios | |
| dc.contributor.author | Votis, Konstantinos | |
| dc.contributor.author | Giakoumis, Dimitrios | |
| dc.contributor.author | Tzovaras, Dimitrios | |
| dc.contributor.author | Chen, Liming | |
| dc.contributor.author | Hamzaoui, Raouf | |
| dc.date.acceptance | 2019-08-22 | |
| dc.date.accessioned | 2019-09-20T08:16:45Z | |
| dc.date.available | 2019-09-20T08:16:45Z | |
| dc.date.issued | 2019 | |
| dc.description | Institute of Engineering Sciences The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | en |
| dc.description.abstract | Environmental sound signals are multi-source, heterogeneous, and varying in time. Many systems have been proposed to process such signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. This paper contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the signal-to-noise-ratio and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D Convolutional Neural Networks (CNN) using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems. The first one, which uses a gradient boosting classifier, achieved an F1-Score of 90.2% and a recognition accuracy of 91.7%. The second one, which uses a 2D CNN with mel-spectrogram images, achieved an F1-Score of 92.7% and a recognition accuracy of 96%. | en |
| dc.funder | European Union (EU) Horizon 2020 | en |
| dc.identifier.citation | Vafeiadis, A., Votis, K., Giakoumis, D., Tzovaras, D., Chen, L. and Hamzaoui, R. (2019) Audio content analysis for unobtrusive event detection in smart homes. Engineering Applications of Artificial Intelligence, in press. | en |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/18477 | |
| dc.language.iso | en_US | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | Marie Skłodowska-Curie grant agreement No. 676157, project ACROSSING | en |
| dc.publisher | Elsevier | en |
| dc.researchinstitute | Cyber Technology Institute (CTI) | en |
| dc.subject | Smart homes | en |
| dc.subject | Ambient assisted living | en |
| dc.subject | Audio signal processing | en |
| dc.subject | Feature extraction | en |
| dc.subject | Feature selection | en |
| dc.subject | Deep learning | en |
| dc.title | Audio Content Analysis for Unobtrusive Event Detection in Smart Homes | en |
| dc.type | Article | en |