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Detail
Journal Article
Efficient pitch‐estimation network for edge devices
- Authors
- Chi Yoon Jeong, Youngmi Song, Sungyong Shin, Mooseop Kim
- Issue Date
- 2025-02
- Citation
- ETRI Journal, v.47, no.1, pp.112-122
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Attention mechanism, Audio signal, Conventional methods, Convolutional networks, Deep neural network(DNN), Edge devices, High performance, Low-Power Devices, Network Architecture, Proposed model, Public Datasets
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
