@article{MessmerReichOuldAbdeslam2023,
  author    = {Liane-Marina Me{\"s}mer and Christoph Reich and Djaffar Ould-Abdeslam},
  title     = {Context-aware Acoustic Signal Processing},
  series   = {Procedia Computer Science},
  volume    = {225.2023},
  issn      = {1877-0509},
  doi       = {10.1016/j.procs.2023.10.095},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:fn1-opus4-103189},
  pages     = {1073 -- 1082},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}