Depression Detection and Recognition Research Based on Audio Analysis and Artificial Intelligence Algorithms

• Published in: 2024 IEEE 1st International Workshop on Future Intelligent Technologies for Young Researchers (FITYR) pp. 67 - 72
• Main Authors: Wang, Zhuozheng, Wang, Yunlong, Zhao, Xixi, Chen, Bingxu, Chen, Haonan, Wang, Gang, Feng, Lei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.07.2024
• Subjects: Accuracy; cnn; Convolutional neural networks; Deep learning; Depression; depression recognition; Feature extraction; Radio frequency; random forest; Random forests; Resistance; Support vector machines; svm; Testing
• DOI: 10.1109/FITYR63263.2024.00017

In response to the prevalent symptoms of depression, the complexity of diagnostic processes, and individuals' resistance to psychological testing in contemporary society, this study proposes an automated depression detection method based on audio analysis. This method aims to enhance the accuracy of depression state recognition by comparing the efficiency of different models, including machine learning and deep learning. Initially, the study collected audio sample data from 50 individuals, including both healthy subjects and patients with depression. During the audio data processing phase, features related to depression recognition, such as Mel-frequency cepstral coefficients (MFCC) and fundamental frequency, were extracted from these samples. Subsequently, a series of recognition comparison experiments were conducted based on the extracted features, involving various algorithmic models such as Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN). Experimental results demonstrated that the Convolutional Neural Network exhibited higher accuracy in recognizing depression states from audio data, with an average recognition accuracy rate of 95.82%. This finding indicates that deep learning models, especially Convolutional Neural Networks, have significant advantages in addressing such issues, providing robust technical support for the future automatic detection of depression.