Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

Significant efforts are being invested to bring state-of-the-art classification and recognition to edge devices with extreme resource constraints (memory, speed and lack of GPU support). Here, we demonstrate the first deep network for acoustic recognition that is small enough for an off-the-shelf microcrocontroller, yet achieves state-of-the-art performance on standard benchmarks. Rather than handcrafting a once-off solution, we present a universal pipeline that converts a large deep convolutional network automatically via compression and quantization into a network for resource-impoverished edge devices. After introducing ACDNet, which produces above state-of-the-art accuracy on ESC-10 (96.65%) and ESC-50 (87.1%), we describe the compression pipeline and show that it allows us to achieve 97.22% size reduction and 97.28% FLOP reduction while maintaining close to state-of-the-art accuracy (83.65% on ESC-50). We describe a successful implementation on a standard off-the-shelf microcontroller and, beyond laboratory benchmarks, report successful tests on real-world data sets.

Environmental Sound Classification on the Edge: Deep Acoustic Networks for Extremely Resource-Constrained Devices

Significant efforts are being invested to bring the classification and recognition powers of desktop and cloud systems directly to edge devices. The main challenge for deep learning on the edge is to handle extreme resource constraints(memory, CPU speed and lack of GPU support). We present an edge solution for audio classification that achieves close to state-of-the-art performance on ESC-50, the same benchmark used to assess large, non resource-constrained networks. Importantly, we do not specifically engineer the network for edge devices. Rather, we present a universal pipeline that converts a large deep convolutional neural network (CNN) automatically via compression and quantization into a network suitable for resource-impoverished edge devices. We first introduce a new sound classification architecture, ACDNet, that produces above state-of-the-art accuracy on both ESC-10 and ESC-50 which are 96.75% and 87.05% respectively. We then compress ACDNet using a novel network-independent approach to obtain an extremely small model. Despite 97.22% size reduction and 97.28% reduction in FLOPs, the compressed network still achieves 82.90% accuracy on ESC-50, staying close to the state-of-the-art. Using 8-bit quantization, we deploy ACDNet on standard microcontroller units (MCUs). To the best of our knowledge, this is the first time that a deep network for sound classification of 50 classes has successfully been deployed on an edge device. While this should be of interestin its own right, we believe it to be of particular importance that this has been achieved with a universal conversion pipeline rather than hand-crafting a network for minimal size.