Chunyan Zeng, Yuhao Zhao, Zhifeng Wang, Kun Li, Xiangkui Wan, Min Liu
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Our study not only demonstrates a relative improvement of approximately 1.5% in all four evaluation metrics over traditional convolutional neural networks but also compares the performance across two public datasets. Furthermore, we delve into the self-attention mechanism’s adaptability across different network architectures by embedding the Squeeze-and-Excitation mechanism within both residual and conventional convolutional network frameworks. Through ablation studies and comparative analyses, we reveal that the impact of self-attention mechanisms varies significantly with the underlying network architecture. Additionally, employing a transfer learning strategy has allowed us to leverage data from a baseline network with extensive samples, applying it to a smaller dataset to successfully identify 141 devices. This approach resulted in performance enhancements ranging from 4% to 7% across various metrics, highlighting the transfer learning method’s role in advancing digital audio source identification research. These findings not only validate the Squeeze-and-Excitation self-attention mechanism’s effectiveness in audio source recognition but also illustrate the broader applicability and benefits of incorporating advanced learning strategies in overcoming data scarcity and enhancing model adaptability.</p>","PeriodicalId":10227,"journal":{"name":"Circuits, Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Squeeze-and-Excitation Self-Attention Mechanism Enhanced Digital Audio Source Recognition Based on Transfer Learning\",\"authors\":\"Chunyan Zeng, Yuhao Zhao, Zhifeng Wang, Kun Li, Xiangkui Wan, Min Liu\",\"doi\":\"10.1007/s00034-024-02850-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Recent advances in digital audio source recognition, particularly within judicial forensics and intellectual property rights domains, have been significantly propelled by deep learning technologies. As these methods evolve, they introduce novel models and enhance processing capabilities crucial for audio source recognition research. Despite these advancements, the limited availability of high-quality labeled samples and the labor-intensive nature of data labeling remain substantial challenges. This paper addresses these challenges by exploring the efficacy of self-attention mechanisms, specifically through a novel neural network that integrates the Squeeze-and-Excitation (SE) self-attention mechanism for identifying recording devices. Our study not only demonstrates a relative improvement of approximately 1.5% in all four evaluation metrics over traditional convolutional neural networks but also compares the performance across two public datasets. Furthermore, we delve into the self-attention mechanism’s adaptability across different network architectures by embedding the Squeeze-and-Excitation mechanism within both residual and conventional convolutional network frameworks. Through ablation studies and comparative analyses, we reveal that the impact of self-attention mechanisms varies significantly with the underlying network architecture. Additionally, employing a transfer learning strategy has allowed us to leverage data from a baseline network with extensive samples, applying it to a smaller dataset to successfully identify 141 devices. This approach resulted in performance enhancements ranging from 4% to 7% across various metrics, highlighting the transfer learning method’s role in advancing digital audio source identification research. 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Squeeze-and-Excitation Self-Attention Mechanism Enhanced Digital Audio Source Recognition Based on Transfer Learning
Recent advances in digital audio source recognition, particularly within judicial forensics and intellectual property rights domains, have been significantly propelled by deep learning technologies. As these methods evolve, they introduce novel models and enhance processing capabilities crucial for audio source recognition research. Despite these advancements, the limited availability of high-quality labeled samples and the labor-intensive nature of data labeling remain substantial challenges. This paper addresses these challenges by exploring the efficacy of self-attention mechanisms, specifically through a novel neural network that integrates the Squeeze-and-Excitation (SE) self-attention mechanism for identifying recording devices. Our study not only demonstrates a relative improvement of approximately 1.5% in all four evaluation metrics over traditional convolutional neural networks but also compares the performance across two public datasets. Furthermore, we delve into the self-attention mechanism’s adaptability across different network architectures by embedding the Squeeze-and-Excitation mechanism within both residual and conventional convolutional network frameworks. Through ablation studies and comparative analyses, we reveal that the impact of self-attention mechanisms varies significantly with the underlying network architecture. Additionally, employing a transfer learning strategy has allowed us to leverage data from a baseline network with extensive samples, applying it to a smaller dataset to successfully identify 141 devices. This approach resulted in performance enhancements ranging from 4% to 7% across various metrics, highlighting the transfer learning method’s role in advancing digital audio source identification research. These findings not only validate the Squeeze-and-Excitation self-attention mechanism’s effectiveness in audio source recognition but also illustrate the broader applicability and benefits of incorporating advanced learning strategies in overcoming data scarcity and enhancing model adaptability.
期刊介绍:
Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area.
The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing.
The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published.
Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.