Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification
Why this work is in the frame
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Bibliographic record
Abstract
The popular frequency-domain features Mel-frequency cepstral coefficients (MFCCs) have been widely used for the task of acoustic scene classification (ASC). The MFCC feature vector describes only the power spectral envelope of a single frame, but it seems like environmental audio signal would benefit from information in the temporal dynamics. However, the classic approach of integrating them would lose this important information. Here, we adopt local binary pattern (LBP) as a tool to characterize the latent information on the temporal dynamics. The frame-level MFCC features are viewed as a 2-D image, where we use LBP to encode the evolution process. Besides, some complementary spectral features such as spectral centroid (SC), spectral bandwidth (SBW) is utilized to further improve the ASC performance. The proposed features are then fed into an ensemble classifier called D3C for recognizing environmental sounds. The results show that the proposed method was able to achieve a classification improvement of 8% compared to the baseline system. Our work presented a new method for combing the temporal features, demonstrating the significance of the temporal evolution features for characterizing the environmental sound.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 it