A Knowledge Representation Framework for Context-Dependent Audio Processing

Fazekas G, Wilmering T, Sandler M. A Knowledge Representation Framework for Context-Dependent Audio Processing. In: AES Conference: 42nd International Conference: Semantic Audio. Audio Engineering Society; 2011. Paper 8-2. Available from: https://aes.org/publications/elibrary-page/?id=15967

@inproceedings{Fazekas2011_15967,
  author = {Fazekas, Gyorgy and Wilmering, Thomas and Sandler, Mark},
  title = {{A Knowledge Representation Framework for Context-Dependent Audio Processing}},
  booktitle = {AES Conference: 42nd International Conference: Semantic Audio},
  note = {Paper 8-2},
  year = {2011},
  month = jul,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15967}
}

TY  - CPAPER
TI  - A Knowledge Representation Framework for Context-Dependent Audio Processing
AU  - Fazekas, Gyorgy
AU  - Wilmering, Thomas
AU  - Sandler, Mark
T2  - AES Conference: 42nd International Conference: Semantic Audio
M1  - Paper 8-2
PY  - 2011
DA  - 2011/07/06
UR  - https://aes.org/publications/elibrary-page/?id=15967
PB  - Audio Engineering Society
LA  - en
AB  - This paper presents a general framework for using appropriately structured information about audio recordings in music processing, and shows how this framework can be utilised in multitrack music production tools. The information, often referred to as metadata, is commonly represented in a highly domain and application specific format. This prevents interoperability and its ubiquitous use across applications. In this paper, we address this issue. The basis for the formalism we use is provided by Semantic Web ontologies rooted in formal logic. A set of ontologies are used to describe structured representation of information such as tempo, the name of instruments or onset times extracted from audio. This information is linked to audio tracks in music production environments as well as processing blocks such as audio effects. We also present specific case studies, for example, the use of audio effects capable of processing and predicting metadata associated with the processed signals. We show how this increases the accuracy of description, and reduces the computational cost, by omitting repeated application of feature extraction algorithms.
ER  -