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ChordCraft: Dataset-Driven Music Analysis Using Convolutional Neural Networks
Abstract
This study explores the intersection of artificial intelligence and music theory by focusing on automatic chord identification. Central to this work is the ChordCraft dataset, a purpose-built resource designed for in-depth analysis of harmonic structures, including chord transitions and stylistic variations across genres. We implement a framework based on Convolutional Neural Networks (CNNs), which excel at pattern recognition in spectral and temporal audio data. By systematically analyzing chord progressions and harmonic layers, the model identifies latent features that correspond to distinct musical signatures. This research contributes to AI-driven music analysis by showing how deep learning can decode the complex language of harmony. It highlights the potential of computational tools to augment musicological research and assist composers, educators, and producers, ultimately opening new pathways for intelligent systems in contemporary music.
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