This repository hosts the core research and architectural frameworks for my publications in IEEE Access (2024) and IEEE ICCE-Asia (2022). The focus is on leveraging Information Fusion strategies to achieve state-of-the-art accuracy in music genre classification.
"A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features" Published in IEEE Access (Vol. 12, 2024)
- Objective: To validate the superiority of Late Fusion strategies over existing CNN models.
- Large-Scale Benchmark: Conducted an empirical study on 12 public datasets (including Ballroom, ISMIR04, and GTZAN).
- Outcome: The devised Late Fusion CNN outperformed all compared methods, proving that fusing high-level feature maps from parallel branches is more effective for acoustic signal processing.
- 🔗 Read Full Paper on IEEE Xplore
"Effective Music Genre Classification using Late Fusion Convolutional Neural Network with Multiple Spectral Features" Published in IEEE ICCE-Asia 2022
- Focus: Practical implementation of a multi-input CNN using STFT, Mel-Spectrogram, and MFCC.
- Strategy: Developed a late-stage integration layer that captures unique temporal and spectral nuances from each feature type independently before final classification.
- 🔗 Read Full Paper on IEEE Xplore
- Fusion Strategy: Late Fusion (parallel CNN branches with integrated decision layers).
- Feature Extraction: Advanced signal processing using STFT, Mel-Spectrograms, and MFCC.
- Experimental Rigour: Validated across 12 diverse datasets to ensure model generalisation and robustness.
My research in Late Fusion is directly applicable to real-world challenges where decision-making requires integrating heterogeneous data sources (e.g., combining user metadata with behavioral signals in Ad-Tech). Managing 12+ datasets also demonstrates my ability to handle complex data pipelines and ensure model performance at scale.