Efficient pitch-estimation network for edge devices

Abstract

Pitch estimation is the task of finding the most conspicuous frequency in a complex audio signal. Many methods that use deep neural networks have significantly increased the accuracy of pitch estimation; however, their real-time performance results were achieved on high-performance devices. Because pitch estimation is widely used in real-time applications on low-power devices, we propose an efficient method for estimating pitch on edge devices. The network architecture of the proposed method uses a depth-scaling strategy and fully leverages convolutional networks. We further introduce a channel attention mechanism to increase accuracy without increasing computational overhead. We compared the proposed model with state-of-the-art (SOTA) and conventional methods using two public datasets. The experimental results show that the proposed method has a better classification accuracy than FCNF0++, which is the best performing SOTA model. Furthermore, it reduces the processing time obtained by FCNF0++ on a personal computer and two edge devices by 48% on average. These experimental results confirm that the proposed method efficiently classifies pitch on edge devices.