TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Meßmer, Liane-Marina
A1  - Reich, Christoph
A1  - Ould-Abdeslam, Djaffar
T1  - Context-aware Acoustic Signal Processing
BT  - 7th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)
JF  - Procedia Computer Science
N2  - 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.
Y1  - 2023
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-103189
SN  - 1877-0509
SS  - 1877-0509
U6  - https://doi.org/10.1016/j.procs.2023.10.095
DO  - https://doi.org/10.1016/j.procs.2023.10.095
VL  - 225.2023
SP  - 1073
EP  - 1082
ER  -