Digital and AI-Based Frameworks for Flute Music Analysis and Genre Classification
Keywords:
Flute analysis, Convolutional neural networks, MFCC features, Music genre classification, Digital signal processing.Abstract
The integration of artificial intelligence and digital signal processing technologies has revolutionized music analysis, particularly in wind instrument recognition and classification. This study explores comprehensive digital and AI-based frameworks for flute music analysis and genre classification using advanced machine learning techniques. The research employs convolutional neural networks (CNNs) combined with multiple spectrogram representations including MFCC, Log-Mel, STFT, Chroma, Spectral Contrast, and Tonnetz for enhanced feature extraction from flute audio recordings. Our methodology utilizes a dataset of 6,705 audio samples from various music information retrieval databases, implementing deep learning architectures with multi-layer feature extraction capabilities. Results demonstrate that MFCC spectrograms achieve the highest classification accuracy of 62% for flute instrument recognition, while Log-Mel spectrograms show superior performance with 55% accuracy specifically for flute and mallet instrument classification. The developed framework successfully classifies flute music across multiple genres including classical, folk, contemporary, and world music traditions with statistical significance. This research contributes to advancing automated music information retrieval systems and provides a robust foundation for intelligent music analysis applications in digital audio processing and music education technologies.
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References
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