Speakers
Description
Edge cloud applications have become vital as outdated cloud architectures face challenges in handling increasing data volumes, especially for audio signals. This article reports on a simple edge cloud architecture for real-time environmental audio classification to improve indoor security and availability. Audio signals are captured at the edge layer using a Raspberry Pi, then converted into Mel spectrograms using the Librosa Python library, and subsequently transmitted to a cloud-hosted convolutional neural network (CNN) trained on the FSD50K dataset. The application achieves 84\% overall accuracy with low latency, efficiently managing resource constraints, and scalability. This application presents real-time images and alerts, indicating the system's ability to detect and support emergencies on time for hearing-impaired users (clients).
