Audio Data Augmentation for Road Objects Classification by an Artificial Neural Network

Barak O, Sallem N. Audio Data Augmentation for Road Objects Classification by an Artificial Neural Network. In: AES Convention 147. Audio Engineering Society; 2019.pp. 10269:1-. Available from: https://aes.org/publications/elibrary-page/?id=20642

@inproceedings{Barak2019_20642,
  author = {Barak, Ohad and Sallem, Nizar},
  title = {{Audio Data Augmentation for Road Objects Classification by an Artificial Neural Network}},
  booktitle = {AES Convention 147},
  pages = {10269:1-},
  year = {2019},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20642}
}

TY  - CPAPER
TI  - Audio Data Augmentation for Road Objects Classification by an Artificial Neural Network
AU  - Barak, Ohad
AU  - Sallem, Nizar
T2  - AES Convention 147
SP  - 1
M1  - Paper 10269
PY  - 2019
DA  - 2019/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20642
PB  - Audio Engineering Society
LA  - en
AB  - Following the resurgence of machine learning within the context of autonomous driving, the need for acquiring and labeling data expanded by folds. Despite the large amount of available visual data (images, point clouds, . . . ), researchers apply augmentation techniques to extend the training dataset, which improves the classification accuracy. When trying to exploit audio data for autonomous driving, two challenges immediately surfaced: first, the lack of available data and second, the absence of augmentation techniques. In this paper we introduce a series of augmentation techniques suitable for audio data. We apply several procedures, inspired by data augmentation for image classification, that transform and distort the original data to produce similar effects on sound. We show the increase in overall accuracy of our neural network for sound classification by comparing it to the non-augmented version.
ER  -