Developing an Algorithm for Generating a Tabla Accompaniment for Hindustani Music
Bibliographic record
Abstract
Indian classical music has been a key cornerstone of the Indian identity and its diasporas around the world. Hindustani music has always been played as vocal and instrumental music for festivities and cultural events. It plays a key role in all religious occasions. In many developing countries encompassing the Indian diaspora, there has been a decline in the number of skilled and amateur musicians of instruments such as the Harmonium, Sitar, Tabla and other classical Indian musical instruments. This trend can be easily noticed during religious and cultural events in which there is a lack of musical accompaniment for vocalists. To address this limitation, we have created a low-cost tool that can analyse the melodies and rhythm in a song and generate a Tabla accompaniment to it in real time. This system was developed using Fast Fourier Transformation (FFT), onset analysis and autocorrelation to identify the tempo (BPM), taal (beat cycle length), and sam (start of song cycle) from an audio sample at the beginning of the song. A corresponding tabla accompaniment is then generated and played based on the features identified from the song. This approach only requires a mobile phone for capturing the audio and processing the data, and a Bluetooth speaker for playing the Tabla accompaniment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".