Classification of Emotion with Audio Analysis
Bibliographic record
Abstract
Classification is an important technique used to distinguish data samples. The aim of this study is to classify according to emotions by extracting audio features. Two male and two female individuals expressed four different emotions as "fun", "angry", "neutral" and "sleepy" in the voice data. We used to “MFCC” as a Cepstral feature, “Centroid, Flatness, Skewness, Crest, Flux, Slope, Decrease, Kurtosis, Spread, Entropy, roll off point” as Spectral Feature, “Pitch, Harmonic ratio” as Periodicity Features in the sound features. After, we applied to the data that all the classification algorithms located in the classification learner toolbox in Matlab and we tried to classify the emotion with the algorithm that provides the highest accuracy. Each data in the classification study has twenty-six features inputs and one labeled output value. According to the results, support vector machine algorithm provided the highest accuracy performance. Considering the performances obtained, this study reveals that it is possible to distinguish and classify sounds using sentimental data and sound feature parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".