Multichannel Fusion and Audio-Based Features for Acoustic Event Classification

Krause D, Kowalczyk K. Multichannel Fusion and Audio-Based Features for Acoustic Event Classification. In: AES Convention 145. Audio Engineering Society; 2018. Paper 10103. Available from: https://aes.org/publications/elibrary-page/?id=19829

@inproceedings{Krause2018_19829,
  author = {Krause, Daniel and Kowalczyk, Konrad},
  title = {{Multichannel Fusion and Audio-Based Features for Acoustic Event Classification}},
  booktitle = {AES Convention 145},
  note = {Paper 10103},
  year = {2018},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19829}
}

TY  - CPAPER
TI  - Multichannel Fusion and Audio-Based Features for Acoustic Event Classification
AU  - Krause, Daniel
AU  - Kowalczyk, Konrad
T2  - AES Convention 145
M1  - Paper 10103
PY  - 2018
DA  - 2018/10/06
UR  - https://aes.org/publications/elibrary-page/?id=19829
PB  - Audio Engineering Society
LA  - en
AB  - Acoustic event classification is of interest for various audio applications. The aim of this paper is to investigate the usage of a number of speech and audio based features in the task of acoustic event classification. Several features that originate from audio signal analysis are compared with features typically used in speech processing such as mel-frequency cepstral coefficients (MFCCs). In addition, the approaches to fuse the information obtained from multichannel recordings of an acoustic event are investigated. Experiments are performed using a Gaussian mixture model (GMM) classifier and audio signals recorded using several scattered microphones.
ER  -