Scalable neural architectures for end-to-end environmental sound classification

Sound Event Detection (SED) 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