Week 10: A System That Listens—and Plays

This final week marked the completion of the Tabla AI project. What began as a question—can a machine understand Indian classical rhythm?—has grown into a working system that doesn’t just analyze taal, but actively performs it. The model now listens to real vocal recordings, identifies the rhythmic structure, and overlays tabla accompaniment that aligns with the music. It’s functional, demonstrable, and complete.

Clean Data, Clean Results

After facing data quality issues last week, I reached out to a researcher at IIT Kanpur who generously shared a curated dataset of taal-aligned recordings. Their dataset, complete with accurately marked sam and khali points, was exactly what I needed to finalize and validate my model. With this cleaner data, I retrained and fine-tuned the system, and the model’s performance on vocal recordings improved noticeably—especially in terms of rhythmic alignment and cycle prediction.

The Final App: Overlaying Tabla in Real Time

This week was all about pulling the pieces together into a finished product. I completed the full-stack web app, which now does the following:

1. Accepts a vocal recording (in Teentaal).

2. Uses the trained model to identify the structure of the taal—specifically, the positions of sam and khali.

3. Dynamically overlays a synthesized tabla track that matches the predicted rhythmic cycle.

Reflection

Over ten weeks, the system evolved from basic audio processing to deep musical understanding. It learned to identify rhythmic downbeats, distinguish between types of tabla strokes, analyze melodic phrasing, and now—finally—accompany a performer.

This project brought together everything I love: classical music, technical rigor, machine learning, and creativity. It taught me how to listen more closely, both as a musician and a developer.

The machine now listens. And for the first time—it plays back.