Audio-based Material Classification Using Hybrid CNN and Logistic Regression

Sun, 15 Dec 2024 00:00:00 +0000

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.

Collected 520 real-world samples using smartphone recordings with varying knock strengths. Applied noise reduction and feature extraction (MFCC, temporal, spectral features). Evaluated over diverse CNN combinations, demonstrating effective integration of deep learning with traditional methods. Proposed improvements include attention mechanisms, mixup augmentation, and expanded data collection for better generalization.

基于混合 CNN 与 Logistic Regression 的音频材质分类

Sun, 15 Dec 2024 00:00:00 +0000

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。

使用智能手机录制了 520 个不同敲击力度的真实样本。应用了降噪和特征提取（MFCC、时域特征、频域特征）。在多种 CNN 组合上进行了评估，展示了深度学习与传统方法的有效融合。提出的改进方向包括 Attention 机制、Mixup 数据增强以及扩展数据采集以提升泛化能力。

Logistic Regression | Junfei Zhan's Website

Audio-based Material Classification Using Hybrid CNN and Logistic Regression

基于混合 CNN 与 Logistic Regression 的音频材质分类