Audio Information Mining – Pragmatic Review, Outlook, and a Universal Open Architecture

Duncan PJ, Mohammed DY, Li FF. Audio Information Mining – Pragmatic Review, Outlook, and a Universal Open Architecture. In: AES Convention 136. Audio Engineering Society; 2014. Paper 9075. Available from: https://aes.org/publications/elibrary-page/?id=17222

@inproceedings{Duncan2014_17222,
  author = {Duncan, Philip J. and Mohammed, Duraid Y. and Li, Francis F.},
  title = {{Audio Information Mining – Pragmatic Review, Outlook, and a Universal Open Architecture}},
  booktitle = {AES Convention 136},
  note = {Paper 9075},
  year = {2014},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17222}
}

TY  - CPAPER
TI  - Audio Information Mining – Pragmatic Review, Outlook, and a Universal Open Architecture
AU  - Duncan, Philip J.
AU  - Mohammed, Duraid Y.
AU  - Li, Francis F.
T2  - AES Convention 136
M1  - Paper 9075
PY  - 2014
DA  - 2014/04/06
UR  - https://aes.org/publications/elibrary-page/?id=17222
PB  - Audio Engineering Society
LA  - en
AB  - There is an immense amount of audio data available currently whose content is unspecified and the problem of classification and generation of metadata poses a significant and challenging research problem. We present a review of past and current work in this field; specifically in the three principal areas of segmentation, feature extraction, and classification and give an overview and critical appraisal of techniques currently in use. One of the major impediments to progress in the field has been specialism and the inability of classifiers to generalize, and we propose a non exclusive generalized open architecture framework for classification of audio data that will accommodate third party plugins and work with multi-dimensional feature/descriptor space as input.
ER  -