Semi-Supervised Noise Classification Based on Auto-Encoder

2021 ◽  
Author(s):  
Ni Haiyan ◽  
Wang Wenbo ◽  
Zhao Meng ◽  
Ren Qunyan ◽  
Ma Li
Keyword(s):  
Noise Classification

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

Noise Classification Based on GMM and AANN

2011 ◽  
Vol 58-60 ◽  
pp. 1847-1853 ◽  
Author(s):  
Yan Zhang ◽  
Cun Bao Chen ◽  
Li Zhao
Keyword(s):  
Neural Network ◽  
Maximum Likelihood ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Specific Method ◽  
Accuracy Rate ◽  
Noise Classification ◽  
Distribution Type

In this paper, Gaussian Mixture model (GMM) as specific method is applied to noise classification. On this basis, a modified Gaussian Mixture Model with an embedded Auto-Associate Neural Network (AANN) is proposed. It integrates the merits of GMM and AANN. We train GMM and AANN as a whole and they are trained by means of Maximum Likelihood (ML). In the process of training, the parameter of GMM and AANN are updated alternately. AANN reshapes the distribution of the data and improves the similarity of the feature data in the same distribution type of noise. Experiments show that the GMM with embedded AANN improves accuracy rate of noise classification against baseline GMM.

A machine learning-based underwater noise classification method

2021 ◽  
Vol 184 ◽  
pp. 108333
Author(s):  
Guoli Song ◽  
Xinyi Guo ◽  
Wenbo Wang ◽  
Qunyan Ren ◽  
Jun Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Method ◽  
Underwater Noise ◽  
Noise Classification
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