Honeybees swarming detection approach by sound signal processing

2020 ◽  
Author(s):  
Aleksandra Zlatkova ◽  
Zivko Kokolanski ◽  
Dimitar Tashkovski
Keyword(s):  
Signal Processing ◽  
Sound Signal ◽  
Detection Approach

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Sound Signal Processing

1984 ◽  
Vol 97 (sup413) ◽  
pp. 95-104
Author(s):  
C. H. Chouard ◽  
B. Meyer ◽  
F. Chabolle ◽  
N. Alcaras ◽  
D. Gegu
Keyword(s):  
Signal Processing ◽  
Sound Signal

scholarly journals Model-Based Segmentation and Analysis of Knee Sound Signals

2021 ◽  
Author(s):  
Farhana Parveen
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Knee Joint ◽  
Classification Accuracy ◽  
Signal Analysis ◽  
Early Stage ◽  
Sound Signal ◽  
Noninvasive Diagnosis ◽  
Model Based ◽  
Leave One Out

The motivation of the work is to develop a signal processing methodology for noninvasive diagnosis of knee osteoarthritis in an early stage. The sound signal that is emitted from knee when it moves is called Vibroathrographic (VAG) signal. Analysis of this sound signal will help in diagnosis of the knee joint problems. In this project a model based approach for sementing the VAG signals, followed by feature extraction and classification is proposed. This could be used to get some indication whether the signal is from a normal knee or from an abnormal knee. The proposed scheme also has the capability for finding the depth of severity of the damage and it can also localize the angle range of the knee swing, where the damage has occurred. As a result, the project gave an accuracy of 70.4% with leave-one-out method. After doing the classification using the segments, finally it has been calculated how many segments from each signal has been correctly identified. A total of 30 knee sound signals from normal and abmoraml knees has been used in this work and out of that 26 signals has been classified properly (either normal or abnormal) and 4 signals got misclassified with a successful classification accuracy of 86.7%.

scholarly journals Nondestructive Detection of Ceramic Products Based on Tapping Sound Signal Feature Recogition

2021 ◽  
Vol 2132 (1) ◽  
pp. 012026
Author(s):  
Liping Liu ◽  
Liucheng Jiang ◽  
Lele Qiao
Keyword(s):  
Signal Processing ◽  
Processing System ◽  
Sound Signal ◽  
Sample Length ◽  
Destructive Testing ◽  
Signal Processing System ◽  
Processing Module ◽  
Linear Poly ◽  
Low Efficiency ◽  
Non Destructive

Abstract Recent studies on the test of ceramic non-destructive testing are mainly based on high cost technologies, image processing and so on, these method possesses some drawback of low efficiency, high cost and so on. What’s more, detecting whether the ceramic products by human through listening to sound of tapping is also effectless. This paper proposed a non-destructive method for ceramic products to solve this problem. This non-destructive method consists of a tapping device and a signal processing module. The tapping device will be applied to generate the tapping sound signal and the signal processing system will be applied to analysis signal. After the process of signal analysis, sample length and peak of spectrum 2 parameters is extracted, then use these parameters to train SVM, the results will be compared with BP neural network (BPNN). The result of experiment shows that SVM with different kernels of linear, poly, rbf, sigmoid respectively reach the accuracy of 96.29%, 96.29%, 46.29%, 93.82%, while BPNN reaches the accuracy of 93.21%. This result proves that SVM can effectively complete the task of identifying defective ceramics, and its performance is better than BPNN.

Early detection of mechanical malfunctions in vehicles using sound signal processing

2022 ◽  
Vol 188 ◽  
pp. 108578
Author(s):  
Amrit Suman ◽  
Chiranjeev Kumar ◽  
Preetam Suman
Keyword(s):  
Signal Processing ◽  
Early Detection ◽  
Sound Signal
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