Environmental Sound Classification via Time–Frequency Attention and Framewise Self-Attention-Based Deep Neural Networks
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Bibliographic record
Abstract
Environmental sound classification (ESC) is crucial to understanding the surroundings in Internet of Things (IoT) applications. The state-of-the-art deep learning approaches do not have good ESC performance when there exists various clutter interference, which is common in IoT scenarios. In this article, we present a novel deep neural network framework based on time–frequency attention and framewise self-attention (TFFS-DNN). It consists of two major novel architectures: 1) gradient and 2) latent feature-based DNN to generate our time–frequency attention, which can locate the relevant time–frequency (i.e., spectral) features accurately, and self-attention normalization DNN to generate our framewise self-attentions which properly indicate the relevance of frames. By conjoining these two sorts of distinct and complementary attentions with spectrograms, we are able to identify the importance or relevance in terms of time, frequency, and frame of the sounds using TFFS-DNN, which helps in distinguishing clutter such as background as well as model interpretation to some extent. Thus, the proposed TFFS-DNN can classify environmental sounds with clutter. The evaluation using four real-world environmental sound data sets demonstrates the superior performance of the proposed framework over several state-of-the-art schemes. Notably, we achieve 79.23% classification accuracy on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UrbanSound</i> data set, a raw environmental sound data set that is full of clutter. The ablation study demonstrates a relative 3%–9% improvement of classification accuracy by the proposed framework over the baseline deep model.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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