Do musical notes correlate with emotions? A neuro-acoustical study with Indian classical music
Bibliographic record
Abstract
The most interesting feature of Indian Classical Music is the existence of Raagas. Each Raaga has its own peculiar ascending and descending movement called the Arohana and Avarohana . Even if two (or more) Raagas are made up of the same notes, the combinational varieties of notes evoke different emotions. In this work, we envisage to study how emotion perception in listeners’ changes when there is an alteration of merely a single note in a pentatonic Raaga and also when a particular note(s) is replaced by its flat/sharp counterpart. Approximately 60 sec recordings were done for two pair of Raaga s which were chosen in a manner such that they are having difference in only one note keeping all others same. The fractal dimension of auditory waveform provides a robust nonlinear quantitative parameter with which the two pair of audio clips can be compared. Also, the emotional appraisal from these two pairs were assessed on the basis of psychological listening tests as well from cognitive response in the form of EEG experiments done on 5 participants. Interesting new results are obtained on how trivial changes in the note structure of a particular Raaga influences human emotion to a large extent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".