Acoustic scene classification using convolutional neural networks and multi-scale multi-feature extraction

Abstract Seismic detection technology has been widely used in safety detection of engineering construction abroad. Although it has just started in the field of engineering in our country, its role is becoming more and more important. Through computer technology, micro-seismic detection can provide accurate data for the construction safety detection of large-scale projects, which has important practical significance for the rapid and effective identification of micro-seismic signals. Based on this, the purpose of this article is to study the feature extraction and classification of microseismic signals based on neural games. This article first summarizes the development status of microseismic monitoring technology. Using traditional convolutional neural networks for analysis, a multi-scale feature fusion network is proposed on the basis of convolutional neural networks and big data, the multi-scale feature fusion network is used to research and analyze microseismic feature extraction and classification. This article systematically explains The principle of microseismic signal acquisition and the construction of multi-scale feature fusion network. And use big data, comparative analysis method, observation method and other research methods to study the theme of this article. Experimental research shows that the db7 wavelet base has little effect on the Megatron signal.

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Rethinking environmental sound classification using convolutional neural networks: optimized parameter tuning of single feature extraction

Neural Computing and Applications ◽

10.1007/s00521-021-06091-7 ◽

2021 ◽

Author(s):

Yousef Abd Al-Hattab ◽

Hasan Firdaus Zaki ◽

Amir Akramin Shafie

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Convolutional Neural Networks ◽

Parameter Tuning ◽

Environmental Sound ◽

Sound Classification ◽

Single Feature

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Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽

10.3390/math9060624 ◽

2021 ◽

Vol 9 (6) ◽

pp. 624

Author(s):

Stefan Rohrmanstorfer ◽

Mikhail Komarov ◽

Felix Mödritscher

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Image Classification ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

State Of The Art ◽

Image Data ◽

Classification Model ◽

Upper Body ◽

Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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