Bibliographic record
Abstract
Urban sound classification has a wide range of applications in noise analysis and monitoring systems. In order to solve the problem of low recognition rate of sound classification model under low signal-to-noise ratio (SNR) conditions, on the basis of traditional sound classification fusion model, this paper proposed a new parallel hybrid model based on MGCC and CNN-BiLSTM fusion by making full use of the anti-noise performance of each feature extraction method. Firstly, Mel-frequency cepstral coefficients and Gammatone frequency cepstral coefficients were extracted from the preprocessed sound signals, and the two sound features were input into the Bi-directional Long Short- Term Memory Network combined with Convolutional neural network (CNN-BiLSTM) model as input to further extract deeper features. And the fusion features were obtained after the fusion of the two features. Finally, the fused features were input into a deep neural network for sound classification and recognition. The experimental results show that the accuracy of the proposed method on the Urbansound 8K dataset and the NoiseX-92 noise database can reach 98.4% and the accuracy on the noise dataset can also reach more than 89.6%. Experimental results show that the proposed method still has high recognition accuracy in low signal-to-noise ratio, and its performance is better than the traditional sound fusion recognition model.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".