Abstract
Sound event detection is intended to analyze and
recognize the sound events in audio streams and it
has widespread applications in real life. Recently,
deep neural networks such as convolutional recurrent neural networks have shown state-of-the-art
performance in this task. However, the previous
methods were designed and implemented on devices with rich computing resources, and there are
few applications on mobile devices. This paper focuses on the solution on the mobile platform for
sound event detection. The architecture of the solution includes offline training and online detection.
During offline training process, multi model-based
distillation method is used to compress model to
enable real-time detection. The online detection
process includes acquisition of sensor data, processing of audio signals, and detecting and recording of sound events. Finally, we implement an application on the mobile device that can detect sound
events in near real time