Emergency Vehicles Audio Detection and Localization in Autonomous Driving

F. Beritelli, S. Casale, A. Russo, and S. Serrano, “An automatic emergency signal recognition system for the hearing impaired,” in 2006 IEEE 12th Digital Signal Processing Workshop 4th IEEE Signal Processing Education Workshop , 2006, pp. 179–182

2006

Earlier work this paper cites.

F. Meucci, L. Pierucci, E. Del Re, L. Lastrucci, and P. Desii, “A real-time siren detector to improve safety of guide in traffic environment,” in 2008 16th European Signal Processing Conference , 2008, pp. 1–5

2008

Earlier work this paper cites.

D. Carmel, A. Yeshurun, and Y. Moshe, “Detection of alarm sounds in noisy environments,” in 2017 25th European Signal Processing Conference (EUSIPCO) , 2017, pp. 1839–1843

2017

Earlier work this paper cites.

N. Yalta, K. Nakadai, and T. Ogata, “Sound source localization using deep learning models,” Journal of Robotics and Mechatronics , vol. 29, pp. 37–48, 02 2017

2017

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008

2017

Earlier work this paper cites.

Apollo-Platform, “https://github.com/apolloauto/apollo,” Apr 2017

2017

Earlier work this paper cites.

F. Grondin and F. Michaud, “Lightweight and optimized sound source localization and tracking methods for open and closed microphone array configurations,” 2018

2018

Earlier work this paper cites.

J. M. Vera-Diaz, D. Pizarro, and J. M. Guarasa, “Towards end-to-end acoustic localization using deep learning: from audio signal to source position coordinates,” CoRR , vol. abs/1807.11094, 2018. [Online]. Available: http://arxiv.org/abs/1807.11094

Original

2018

Cited alongside, same era.

N. R. Shabtai and E. Tzirkel, “Detecting the direction of emergency vehicle sirens with microphones,” in EAA Spatial Audio Signal Processing Symposium , 2019, pp. 137–142

2019

Cited alongside, same era.

X. Yan and J. Zhao, “Application of improved convolution neural network in financial forecasting,” in 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) . IEEE, 2019, pp. 321–326

2019

Cited alongside, same era.

S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,” Advances in Neural Information Processing Systems , vol. 32, pp. 5243–5253, 2019

2019

Cited alongside, same era.

T. GreyB, “Top 30 autonomous vehicle technology and car companies,” Jun 2021. [Online]. Available: https://www.greyb.com/autonomous-vehicle-companies/

2021

Closest in time.

C. Schymura, B. T. Bönninghoff, T. Ochiai, M. Delcroix, K. Kinoshita, T. Nakatani, S. Araki, and D. Kolossa, “PILOT: introducing transformers for probabilistic sound event localization,” CoRR , vol. abs/2106.03903, 2021. [Online]. Available: https://arxiv.org/abs/2106.03903

Original

2021

Closest in time.

Y. Tay, M. Dehghani, J. Gupta, D. Bahri, V. Aribandi, Z. Qin, and D. Metzler, “Are pre-trained convolutions better than pre-trained transformers?” arXiv preprint arXiv:2105.03322 , 2021

Original

2021

Closest in time.

A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola, “Dive into deep learning,” arXiv preprint arXiv:2106.11342 , 2021

Original

2021

Closest in time.