An Unsupervised Hybrid Approach for Online Detection of Sound Scene Changes in Broadcast Content

Sevkin G, Craciun A, Bäckström T. An Unsupervised Hybrid Approach for Online Detection of Sound Scene Changes in Broadcast Content. In: AES Conference: 2017 AES International Conference on Semantic Audio. Audio Engineering Society; 2017. Paper P1-2. Available from: https://aes.org/publications/elibrary-page/?id=18765

@inproceedings{Sevkin2017_18765,
  author = {Sevkin, Gökhan and Craciun, Alexandra and Bäckström, Tom},
  title = {{An Unsupervised Hybrid Approach for Online Detection of Sound Scene Changes in Broadcast Content}},
  booktitle = {AES Conference: 2017 AES International Conference on Semantic Audio},
  note = {Paper P1-2},
  year = {2017},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18765}
}

TY  - CPAPER
TI  - An Unsupervised Hybrid Approach for Online Detection of Sound Scene Changes in Broadcast Content
AU  - Sevkin, Gökhan
AU  - Craciun, Alexandra
AU  - Bäckström, Tom
T2  - AES Conference: 2017 AES International Conference on Semantic Audio
M1  - Paper P1-2
PY  - 2017
DA  - 2017/06/06
UR  - https://aes.org/publications/elibrary-page/?id=18765
PB  - Audio Engineering Society
LA  - en
AB  - In this paper we describe an online system for broadcast content, which can detect sound scene changes with high accuracy. The system is unsupervised and does not require prior information on the segment classes. A scene change probability score is computed for each frame of the signal using a hybrid approach combining a model-based (Gaussian Mixture Model) with a distance-based (Hotelling’s T2-Statistic) segmentation method. The mixture model parameters are adapted online using the previous frames of the signal. Experiments on real recordings show that we can achieve more than 85% correct segment change detection with only 16% false detections.
ER  -