###### Abstract  
The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio-visual temporal source identification. This paper introduces an ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the sinusoidal activation function and spectral attention mechanism while also redesigning the model framework. Training is conducted using a public dataset on the open science framework (OSF) platform, with final experimental results demonstrating that, after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future research will focus on expanding dataset coverage and diversity to verify the model's generality and robustness across different regions, time spans, and data sources. Additionally, it aims to explore the extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.  
electric network frequency; ENF classification; sinusoidal activation function; spectral attention; UniTS-SinSpec model +
Footnote †: journal: Medical  
sophisticated feature extraction capacities for the automatic acquisition of complex data representations. Nonetheless, extant deep learning approaches encounter constraints when leveraging frequency domain information alongside periodic signal processing capabilities.  
In addressing the challenges posed by traditional signal processing techniques and the limitations of machine learning in ENF regional classification tasks, we refined and advanced the UniTS model through both training and testing using the OSF public dataset.  
* We conduct a comparative analysis of the sinusoidal activation function in relation to commonly utilized activation functions. Our investigation focuses on the impact of activation functions on processing periodic signals, revealing that the sinusoidal activation function leverages the periodic nature of the sinusoidal function to optimize the retention and extraction of periodic features within ENF data, thereby enhancing its efficacy in processing periodic signals.
* We develop a spectral attention module and integrate it into the UniTS model. The spectral attention mechanism is utilized to effectively capture crucial frequency information in the original ENF data within the frequency domain, thereby addressing the model's limited noise resistance to raw data and inadequacies of extracting and learning frequency domain information.
* We propose a standardized dataset configuration and preprocessing approach for ENF classification based on the categorization of ENF regions. Due to the diverse research objectives in previous studies on ENF, variations exist in both the datasets utilized and the processing methods employed. Our proposal aims to establish a unified dataset configuration and preprocessing approach that is better suited for the task of classifying ENF regions.
* In order to validate the efficacy of the enhanced model and investigate the factors influencing its performance, a total of three experiments is devised in this study, with each experiment being further subdivided into multiple groups according to its specific objectives. These objectives encompass an assessment of the fundamental classification performance of the UniTS model, an ablation experiment on the novel enhancements, and a hyperparameter search for the refined model. The ultimate average classification accuracy achieved by the UniTS-SinSpec model is 97.47%.