Broiler sounds can provide feedback on their own body condition, to a certain extent. Aiming at the noise in the sound signals collected in broiler farms, research on evaluating the filtering methods for broiler sound signals from multiple perspectives is proposed, and the best performer can be obtained for broiler sound signal filtering. Multiple perspectives include the signal angle and the recognition angle, which are embodied in three indicators: signal-to-noise ratio (SNR), root mean square error (RMSE), and prediction accuracy. The signal filtering methods used in this study include Basic Spectral Subtraction, Improved Spectral Subtraction based on multi-taper spectrum estimation, Wiener filtering and Sparse Decomposition using both thirty atoms and fifty atoms. In analysis of the signal angle, Improved Spectral Subtraction based on multi-taper spectrum estimation achieved the highest average SNR of 5.5145 and achieved the smallest average RMSE of 0.0508. In analysis of the recognition angle, the kNN classifier and Random Forest classifier achieved the highest average prediction accuracy on the data set established from the sound signals filtered by Wiener filtering, which were 88.83% and 88.69%, respectively. These are significantly higher than those obtained by classifiers on data sets established from sound signals filtered by other methods. Further research shows that after removing the starting noise in the sound signal, Wiener filtering achieved the highest average SNR of 5.6108 and a new RMSE of 0.0551. Finally, in comprehensive analysis of both the signal angle and the recognition angle, this research determined that Wiener filtering is the best broiler sound signal filtering method. This research lays the foundation for follow-up research on extracting classification features from high-quality broiler sound signals to realize broiler health monitoring. At the same time, the research results can be popularized and applied to studies on the detection and processing of livestock and poultry sound signals, which has extremely important reference and practical value.

Pipeline health assessment is an important work in industry, and information on the type and size of defects is an essential basis for assessing the health of a pipeline. Therefore, a pipeline defect estimation method based on supervised learning ensemble model is proposed in this paper. Firstly, several typical feature factors are calculated using feature formulas in the field of acoustics, capable of distinguishing the defect signal variability. Thereafter, Pearson correlation coefficient analysis and Random Forest importance ranking feature analysis methods are utilized to filter out the more valuable features. To improve the performance of defect estimation, a fusion model combining qualitative and quantitative analysis based on Random Forest and XGBoost is constructed to preferentially identify the type of defect signal qualitatively and then predict the size quantitatively. Finally, experimental results and comprehensive analysis with other mainstream supervised learning methods indicate that the prediction error of this method is basically below 1.5%, which addresses the issue of the low estimation accuracy of traditional methods.

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<div>Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data balancing effect than SMOTE, and our optimized RF algorithm achieves an accuracy of over 96% for identifying loose particle signals. The proposed method can also be popularized in the field of loose particle detection for large-scale sealing equipment and other various areas of fault diagnosis based on sound signals.</div>