Scalable neural architectures for end-to-end environmental sound classification
|Francesco Paissan, Alberto Ancilotto, Alessio Brutti, Elisabetta Farella
|Scalable neural architectures for end-to-end environmental sound classification
|Sound Event Detection is a complex task simulating human ability to recognize what is happening in the surrounding from auditory signals only. This technology is a crucial asset in many applications such as smart cities. Here, urban sounds can be detected and processed by embedded devices in an Internet of Things (IoT) to identify meaningful events for municipalities or law enforcement. However, while current deep learning techniques for SED are effective, they are also resource- and power-hungry, thus not appropriate for pervasive battery-powered devices. In this paper, we propose novel neural architectures based on PhiNets for real-time acoustic event detection on microcontroller units. The proposed models are easily scalable to fit the hardware requirements and can operate both on spectrograms and waveforms. In particular, our architectures achieve state-of-the-art performance on UrbanSound8K in spectrogram classification (around 77%) with extreme compression factors 99.8% with respect to current state-of-the-art architectures.
|Electronic ISSN: 2379-190X
Print on Demand(PoD) ISSN: 1520-6149
|IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP)
|Year of Publication, Publisher
- Project Coordinator: Dr. Sotiris Ioannidis
- Institution: Foundation for Research and Technology Hellas (FORTH)
- E-mail: email@example.com
- Start: 01.01.2021
- Duration: 36 months
- Participating Organisations: 17
- Number of countries: 12
This project has received funding from the European Union’s Horizon 2020 Research and Innovation program under grant agreement No 957337. The website reflects only the view of the author(s) and the Commission is not responsible for any use that may be made of the information it contains.