Infinite Music AI

Overview

At Infinite Music, I worked on the design, training, and evaluation of large-scale text-to-music generation systems based on Meta's MusicGen. My role covered the full research pipeline, from constructing structured music datasets to fine-tuning models and testing creative interactions with users.

I focused on improving alignment between text prompts and musical structure, and on shifting generative models toward performer-aware and collaborative systems rather than black-box generators. This work sits at the intersection of machine learning, music cognition, and human-computer interaction.

Key Contributions

• Built and curated large music datasets with tempo, mood, genre, instrumentation, and rhythmic features
• Explored contrastive audio-text representations to analyze prompt alignment and failure modes
• Proposed and implemented remixing features conditioned on extracted percussion patterns
• Worked closely with researchers in computational neuroscience and music performance

Technologies