Audio Classification: Environmental sounds classification
Bibliographic record
Abstract
Recent advancements in the field of machine learning have led to a growing interest in many classification problems, especially involving data in the form of images, video, and audio files. One of the prominent classification problems is to classify sounds and to predict the category of that sound. Some of the applications where such a classification model can be applied in the real world are security systems, classifying music clips to identify the genre of the music, classifying different environmental sounds, speaker detection, and verification. Audio classification is the task of analyzing different audio signals. In this paper, we provide a brief overview of the area of audio classification, describing its system, various modules of feature extraction and modeling, applications, underlying techniques, and some indications of performance. Following this overview, we will discuss some of the strengths and weaknesses of current classification technologies and outline some potential future trends in research, development, and applications. We paid close attention to the inputs, network structures, temporal pooling strategies, and objective functions as these are the fundamental components of many audio classification subtasks. The paper concludes with discussions on future trends and research opportunities in this area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".