Relaxation "sweet spot" exploration in pantophonic musical soundscape using reinforcement learning
Bibliographic record
Abstract
Musical relaxation is a common method to relieve personal stress. Particularly, nature sounds, instrumental music, voice (chanting), "easy listening" songs, etc. can be played for relaxation. Nevertheless, effectiveness of the sounds used for the relaxation is idiosyncratic, depending on personal taste. In our approach, computer-guided audition for spatial soundscapes is investigated, automatically exploring a polyphonic area while using biosignals as indicators of satisfaction. We propose a reinforcement learning (RL) method to discover the sound relaxation "sweet spot" in a polyphonic soundscape. An avatar roams within a pantophonic space, surrounded by six independent audio channels, while a human subject, listening through the avatar's ears, is connected to an electroencephalographic (EEG) headset. Besides the position of the avatar, pitch, reverberation, and filters can also be changed to find the most relaxing virtual standpoint and parameters for the listener. Instead of changing position manually, a Deep Q-Network (DQN) in reinforcement learning is used. An RL agent adjusts parameters according to reward values calculated by change of relative theta band (4--8 Hz) power.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".