whatamilisteningto

This project focuses on music classification using mel-spectrograms, specifically targeting music genre and mood/theme recognition. Spectrograms, visual representations of audio data, are fed into deep learning models for classification. The project utilises popular convolutional neural networks (CNNs) and explores the effectiveness of transfer learning with pre-trained models.

Dataset source

Bogdanov, D., Won M., Tovstogan P., Porter A., & Serra X. (2019). The MTG-Jamendo Dataset for Automatic Music Tagging. Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML 2019).

Dependencies:

• TensorFlow
• Keras
• Matplotlib
• NumPy
• Pandas
• Librosa

Track count

Dataset	Train	Test	Validation	Total
Mood/Theme	11,114	3,699	3,673	18,486
Genre	33,169	11,047	10,999	55,215

Genre Classification Metrics

Model Architecture	Top-4 accuracy	ROC-AUC	PR-AUC	F-Score
VGG19	0.590024	0.886725	0.247987	0.108169
EfficientNetV2-B0	0.568208	0.893232	0.216523	0.067312
Xception	0.604870	0.874622	0.274456	0.145906
DenseNet-201	0.626052	0.902065	0.288641	0.119026

Mood/Theme Classification Metrics

Model Architecture	Top-4 accuracy	ROC-AUC	PR-AUC	F-Score
VGG19	0.407948	0.815694	0.133618	0.100799
EfficientNetV2-B0	0.273317	0.770294	0.060965	0.015179
Xception	0.396593	0.783322	0.131362	0.119081
DenseNet-201	0.425520	0.806734	0.147263	0.116122

Genre Predictions

Mood/Theme Predictions