Environmental Sound Classification Using Local Binary Pattern and Audio Features Collaboration
Bibliographic record
Abstract
This paper presents a new approach to classify environmental sounds using a texture feature local binary pattern (LBP) and audio features collaboration. To our knowledge, this is the first time that the LBP (or its variants), which has a proven track record in the field of image recognition and classification, has been generalized for 1D and combined with audio features for an environmental sound classification task. To this end, we have generalized and defined LBP-1D and local phase quantization (LPQ)-1D on the 1-dimensional (1D) audio signal and have applied the original LBP, the variance LBP (VARLBP) and the extended LBP (ELBP) thus generated to the spectrogram of the audio signal in order to model the sound texture. We have also extensively compared these new LBP-based features to the classical audio descriptors commonly used in environmental sound classification, such as MFCC, GFCC, CQT, chromagram, STE and ZCR. We have evaluated our algorithm on ESC-10 and ESC-50 datasets using classical machine learning algorithms, such as support vector machines (SVM), random forest and k-nearest neighbor (kNN). The results showed that the LBP features outperform the classical audio features. We mix the LBP features with the audio descriptors, and our best mixed model achieves state-of-the-art results for environmental sound classification: 88.5$\%$on ESC-10 and 64.6$\%$on ESC-50. Those results outperform the results of methods that used handcrafted features with classical machine learning algorithms and are similar to some convolutional neural network-based methods. Although our method is not the cutting edge of the state-of-the-art methods, it is faster than any convolutional neural network methods and represents a better choice when there is data scarcity or minimal computing 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".