Deep learning technique based real-time audio event detection experiment in a distributed system architecture

Abstract

The paper investigates a noise-robust and bandwidth-efficient audio event detection technique. In this work, audio features are extracted from audio data using a gammatone filter bank. The proposed denoising autoencoder-based fully connected deep neural network scheme is realized in a distributed architecture. Audio features are extracted using a Raspberry Pi edge computing device and those are fed to a local data server to detect audio events. Experiments have shown that feature extraction and event classification tasks using the proposed technique take 840 ms for a 2-second audio clip that promises real-time implementation. The prototype model shows improved classification probability of noisy audio events with an accuracy of 98.12% and an F1-score of 90.6% at a 10 dB signal-to-noise-ratio. The performance of the proposed technique is compared with state-of-the-art techniques using the Freiburg-106 dataset and improved noise-robust audio event detection performance is found.