  Convolutional neural networks (CNNs) have exhibited state-of-the-art
performance in various audio classification tasks. However, their real-time
deployment remains a challenge on resource-constrained devices like embedded
systems. In this paper, we analyze how the performance of large-scale
pretrained audio neural networks designed for audio pattern recognition changes
when deployed on a hardware such as Raspberry Pi. We empirically study the role
of CPU temperature, microphone quality and audio signal volume on performance.
Our experiments reveal that the continuous CPU usage results in an increased
temperature that can trigger an automated slowdown mechanism in the Raspberry
Pi, impacting inference latency. The quality of a microphone, specifically with
affordable devices like the Google AIY Voice Kit, and audio signal volume, all
affect the system performance. In the course of our investigation, we encounter
substantial complications linked to library compatibility and the unique
processor architecture requirements of the Raspberry Pi, making the process
less straightforward compared to conventional computers (PCs). Our
observations, while presenting challenges, pave the way for future researchers
to develop more compact machine learning models, design heat-dissipative
hardware, and select appropriate microphones when AI models are deployed for
real-time applications on edge devices. All related assets and an interactive
demo can be found on GitHub
