Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".