Опубликована: Дек. 15, 2023
In order to solve the problem that current bearing fault diagnosis model based on voiceprint signal is not enough extract time features, a 3DCNN proposed in this paper. First, Mel-spectrogram used of bearing. Then, diagnose make better use timing information model. Finally, paper has improved precision and recall rate by 6.25% 7.03% respectively compared with classical algorithm. The good accuracy important for engineering practice.
Язык: Английский
Expert Systems with Applications, Год журнала: 2023, Номер 230, С. 120696 - 120696
Опубликована: Июнь 9, 2023
Язык: Английский
Процитировано
125
Automation in Construction, Год журнала: 2024, Номер 165, С. 105485 - 105485
Опубликована: Май 31, 2024
This paper presents a model for sound classification in construction that leverages unique combination of Mel spectrograms and Mel-Frequency Cepstral Coefficient (MFCC) values. combines deep neural networks like Convolution Neural Networks (CNN) Long short-term memory (LSTM) to create CNN-LSTM MFCCs-LSTM architectures, enabling the extraction spectral temporal features from audio data. The data, generated activities real-time closed environment is used evaluate proposed resulted an overall Precision, Recall, F1-score 91%, 89%, respectively. performance surpasses other established models, including Deep (DNN), CNN, Recurrent (RNN), as well these models CNN-DNN, CNN-RNN, CNN-LSTM. These results underscore potential combining MFCC values provide more informative representation thereby enhancing noisy environments.
Язык: Английский
Процитировано
10
Mechanical Systems and Signal Processing, Год журнала: 2024, Номер 214, С. 111411 - 111411
Опубликована: Апрель 11, 2024
Язык: Английский
Процитировано
9
IEEE Access, Год журнала: 2023, Номер 11, С. 60078 - 60108
Опубликована: Янв. 1, 2023
The concept of Acoustic Source Identification (ASI), which refers to the process identifying noise sources has attracted increasing attention in recent years. ASI technology can be used for surveillance, monitoring, and maintenance applications a wide range sectors, such as defence, manufacturing, healthcare, agriculture. signature analysis pattern recognition remain core technologies source identification. Manual identification acoustic signatures, however, become increasingly challenging dataset sizes grow. As result, use Artificial Intelligence (AI) techniques relevant useful. In this paper, we provide comprehensive review AI-based techniques. We analyze strengths weaknesses processes associated methods proposed by researchers literature. Additionally, did detailed survey machinery, underwater applications, environment/event recognition, other fields. also highlight research directions.
Язык: Английский
Процитировано
10
Advanced Engineering Informatics, Год журнала: 2023, Номер 59, С. 102246 - 102246
Опубликована: Ноя. 14, 2023
Язык: Английский
Процитировано
10
Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 143, С. 110023 - 110023
Опубликована: Янв. 13, 2025
Язык: Английский
Процитировано
0
Chinese Journal of Aeronautics, Год журнала: 2025, Номер unknown, С. 103420 - 103420
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Mechanical Systems and Signal Processing, Год журнала: 2025, Номер 231, С. 112680 - 112680
Опубликована: Апрель 8, 2025
Язык: Английский
Процитировано
0
Scientific Journal of Astana IT University, Год журнала: 2025, Номер unknown, С. 172 - 185
Опубликована: Март 30, 2025
Road accidents continue to pose a serious threat public safety, underscoring the need for innovative, automated emergency response systems. This study presents development of mobile application that detects road by analyzing audio signals in real time and immediately sends SMS alerts with GPS coordinates services user-specified contacts. The system comprises two parts: user-facing Android server-side component data processing. To build train detection models, we leverage MIVIA Audio Events dataset applied preprocessing techniques including amplitude normalization, background noise filtering, augmentation. Feature extraction involved zero-crossing rate, spectral centroid, flux, energy entropy, short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCCs). Two classification approaches were investigated: traditional machine learning models (Support Vector Machine, Random Forest, Gradient Boosting) deep model based on convolutional neural networks (CNNs) using Mel spectrogram inputs. Experimental results demonstrate CNN achieved highest performance 91.2% accuracy, 89.5% recall, an F1-score 90.3%, outperforming best classical (Random Forest), which 85.1% accuracy. also reduced average accident alert from 5–7 minutes 1–2 minutes, representing 60–80% improvement speed. These confirm system’s reliability practical benefit, particularly regions like Kazakhstan, where timely medical intervention is critical. Limitations include reliance smartphone availability, internet access, environmental sound conditions. Future work will explore real-world testing, integration accelerometer gyroscope data, deployment edge computing faster on-device Overall, proposed solution cost-effective, scalable approach improving safety saving lives through rapid, detection.
Язык: Английский
Процитировано
0
Journal of Vibration Engineering & Technologies, Год журнала: 2025, Номер 13(5)
Опубликована: Май 29, 2025
Язык: Английский
Процитировано
0