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吠声频率塞普斯特拉尔系数基于悲伤情绪水平识别系统

Barlian Henryranu Prasetio¹, Dhimas Arfian Lazzuardhy¹, Edita Rosana Widasari¹

¹Department of Computer Engineering, Faculty of Computer Science, Universitas Brawijaya, Malang, Indonesia.

Computer methods in biomechanics and biomedical engineering

|November 10, 2025

概括

这项研究引入了一个嵌入式系统,用于识别语言中的悲伤水平,使用Bark-Frequency Cepstral Coefficients (BFCC) 和K-Nearest Neighbors (KNN) 机器学习. 该系统实现了高精度,改善了信号噪声比,并帮助早期诊断精神障碍.

科学领域:

情感计算是一种情感计算.
语言情感识别语言
嵌入式系统嵌入式系统

背景情况:

悲伤具有显著的负面生理和社会影响.
早期识别悲伤对于减轻不良影响至关重要.
现有的情绪识别方法在效率和准确性方面面临挑战.

研究的目的:

开发一个高效的嵌入式系统来识别语言中的不同程度的悲伤.
为了利用Bark-Frequency Cepstral Coefficient (BFCC) 来进行特征提取.
应用机器学习,特别是K-最近邻居 (KNN),用于准确的悲伤分类.

主要方法:

语音输入通过麦克风捕获,并在树Pi上处理.
用于关键特征提取的Bark-Frequency Cepstral Coefficient (BFCC) 用于关键特征提取的时间.
用于优化计算成本和降低噪声的尺寸缩小.
用于分类的K-最近邻居 (KNN) 机器学习模型.
结果通过蓝牙在智能手机应用程序上显示.

主要成果:

该系统显示,与Mel频 Cepstral 系数 (MFCC) 相比,信号与噪声比率 (SNR) 的改善为11.97dB.
具有k=5的KNN模型在识别悲伤水平方面取得了良好的准确率.
该系统特别擅长准确地识别高低水平的悲伤.

结论:

开发的嵌入式系统有效地使用BFCC和KNN对悲伤水平进行分类.
该系统的性能表明,早期诊断精神障碍,如抑郁症和焦虑症的潜力.
这项技术可以有助于减少悲伤对个人的不利影响.

关键词:

美国BFCCCCBFCCCCBBFCCCCBBBFCCCCBBBBFCCBBBBBFCCBB BFCCBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB 在 KNN KNN 标签上.嵌入式系统嵌入式系统识别系统的识别系统悲伤情绪水平悲伤情绪水平

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