Automated Structural Analysis and Harmonic Identification in Chopin Nocturnes via Multi-Modal Deep Learning

**Abstract:** This paper introduces a novel framework for automated structural analysis and harmonic identification in Chopin nocturnes, utilizing a multi-modal deep learning approach. By integrating symbolic music notation (MIDI), audio spectrograms, and human performance transcriptions, our system, "Harmonia," achieves unprecedented accuracy and detail in identifying musical structure, harmonic progressions, and subtle performance nuances within these complex compositions. The framework offers significant advancement in musicological research, automated music transcription, and adaptive music education, potentially influencing the market for digital music tools and analysis software.