Harmonix: A Comprehensive Approach for Advance Music Composition Using AI-ML

Traditional music composition often requires expertise in musical theory and substantial time investment, posing challenges for creators without formal training. Composing traditional music often requires music theory knowledge and commensurate effort, which is hard for non-trained creators. The avoidance of traditional composition methods excludes many talented creators due to the amount of theoretical knowledge and practice needed in order to create music with the traditional compositional processes. Although current tools do not consider the enormous richness of non-Western musical forms, computational approaches provide new options for music creation. We present Harmonix, a system that solves this problem by blending the principles of Indian classical music with modern technology. Our strategy focuses on a framework of composition that is flexible such that users take two of the three components, lyrics, theme or melody, while the system provides the complementary third element. Using mathematical patterns of taal (rhythm), tonal richness of swara (notes) and the dynamic use of lay (tempo), Harmonix preserves the composition structure of Indian classical music with contemporary music influences. The system's neural architecture, which exhibits a unique strength in sustaining long-term melodic coherence, a longstanding challenge in music composition, merges bidirectional LSTM technology with attention mechanisms.