Context-aware Acoustic Signal Processing
- Data processed in context is more meaningful, easier to understand and has higher information content, hence it derives its semantic meaning from the surrounding context. Even in the field of acoustic signal processing. In this work, a Deep Learning based approach using Ensemble Neural Networks to integrate context into a learning system is presented. For this purpose, different use cases are considered and the method is demonstrated using acoustic signal processing of machine sound data for valves, pumps and slide rails. Mel-spectrograms are used to train convolutional neural networks in order to analyse acoustic data using image processing techniques.
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| Author: | Liane-Marina Meßmer, Christoph ReichORCiDGND, Djaffar Ould-Abdeslam |
|---|---|
| URN: | https://urn:nbn:de:bsz:fn1-opus4-103189 |
| DOI: | https://doi.org/10.1016/j.procs.2023.10.095 |
| ISSN: | 1877-0509 |
| Parent Title (English): | Procedia Computer Science |
| Subtitle (German): | 7th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023) |
| Document Type: | Article (peer-reviewed) |
| Language: | English |
| Year of Completion: | 2023 |
| Release Date: | 2024/01/23 |
| Volume: | 225.2023 |
| First Page: | 1073 |
| Last Page: | 1082 |
| Open-Access-Status: | Open Access |
| Gold | |
| Licence (German): |  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |