基于混合 CNN 与 Logistic Regression 的音频材质分类
开发了一种混合模型，用于对七种材质（如桌面、玻璃、黑板）的敲击音频录音进行分类。该模型将基于原始音频的 1D CNN、基于 MFCC 特征的 2D CNN 和 Logistic Regression 组合为集成系统，在评估数据上达到了 94% 的准确率和 0.9426 的加权 F1-score。
Dec 15, 2024
Audio-based Material Classification Using Hybrid CNN and Logistic Regression
Developed a hybrid model for classifying audio recordings of knocking sounds from seven materials (e.g., table, glass, blackboard). The model combines 1D CNN on raw audio, 2D CNN on MFCC features, and logistic regression into an ensemble system. Achieved 94% accuracy and a weighted F1-score of 0.9426 on evaluation data.
Dec 15, 2024