Bibliographic record
Abstract
Recently, more and more studies regarding neural networks have been done. However, the learning process of neural networks is often elusive to human beings, which leads to the advent of feature visualization techniques. Activation Maximization (AM) is one of the feature visualization techniques, originally designed for image data. In AM, the input data is optimized to find the data that activates the selected neuron. In this paper, the emotion recognizer’s output is selected as the neuron, and the latent code of a generator (of Generative Adversarial Networks) is optimized instead of the input raw data. The aim of this study is to apply AM to different representations of audio data (waveform-based data and mel-spectrogram-based data) and different model structures (CNN, WaveNet, LSTM), and to find out the most suitable condition for AM in audio domain data. Additionally, we have also tried to visualize the essential features of being a certain class for emotion classification in speech data, using 2 datasets: the Toronto emotional speech set (TESS) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). The mel-spectrogram-based models were found to be superior to the others, showing the distinctive features of selected emotions. More specifically, the CNN-mel-spectrogram-based model was the best in both qualitative and quantitative (FID score) results. Moreover, as demonstrated in this study, AM can also be employed as an output enhancer for generative models.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".