A Method of Environmental Sound Classification Based on Residual Networks and Data Augmentation

2021 ◽  
pp. 2150018
Author(s):  
Jinfang Zeng ◽  
Youming Li ◽  
Yu Zhang ◽  
Da Chen
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Environmental Sound ◽  
Sound Classification ◽  
Learning Techniques ◽  
Proposed Model ◽  
Audio Data

Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. To date, a variety of signal processing and machine learning techniques have been applied to ESC task, including matrix factorization, dictionary learning, wavelet filterbanks and deep neural networks. It is observed that features extracted from deeper networks tend to achieve higher performance than those extracted from shallow networks. However, in ESC task, only the deep convolutional neural networks (CNNs) which contain several layers are used and the residual networks are ignored, which lead to degradation in the performance. Meanwhile, a possible explanation for the limited exploration of CNNs and the difficulty to improve on simpler models is the relative scarcity of labeled data for ESC. In this paper, a residual network called EnvResNet for the ESC task is proposed. In addition, we propose to use audio data augmentation to overcome the problem of data scarcity. The experiments will be performed on the ESC-50 database. Combined with data augmentation, the proposed model outperforms baseline implementations relying on mel-frequency cepstral coefficients and achieves results comparable to other state-of-the-art approaches in terms of classification accuracy.

scholarly journals Electrocardiogram Classification Based on Deep Convolutional Neural Networks: A Review

2021 ◽  
pp. 43-53
Author(s):  
admin admin ◽  
◽  
◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Machine Learning Techniques ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Good Classification ◽  
Learning Architectures ◽  
Ecg Data

Due to many new medical uses, the value of ECG classification is very demanding. There are some Machine Learning (ML) algorithms currently available that can be used for ECG data processing and classification. The key limitations of these ML studies, however, are the use of heuristic hand-crafted or engineered characteristics of shallow learning architectures. The difficulty lies in the probability of not having the most suitable functionality that will provide this ECG problem with good classification accuracy. One choice suggested is to use deep learning algorithms in which the first layer of CNN acts as a feature. This paper summarizes some of the key approaches of ECG classification in machine learning, assessing them in terms of the characteristics they use, the precision of classification important physiological keys ECG biomarkers derived from machine learning techniques, and statistical modeling and supported simulation.

scholarly journals An Ensemble of Convolutional Neural Networks for Audio Classification

2021 ◽  
Vol 11 (13) ◽  
pp. 5796
Author(s):  
Loris Nanni ◽  
Gianluca Maguolo ◽  
Sheryl Brahnam ◽  
Michelangelo Paci
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Extensive Study ◽  
Urban Environments ◽  
Sound Recognition ◽  
Audio Classification ◽  
Environmental Sound ◽  
Ensembles Of Classifiers ◽  
Sound Classification

Research in sound classification and recognition is rapidly advancing in the field of pattern recognition. One important area in this field is environmental sound recognition, whether it concerns the identification of endangered species in different habitats or the type of interfering noise in urban environments. Since environmental audio datasets are often limited in size, a robust model able to perform well across different datasets is of strong research interest. In this paper, ensembles of classifiers are combined that exploit six data augmentation techniques and four signal representations for retraining five pre-trained convolutional neural networks (CNNs); these ensembles are tested on three freely available environmental audio benchmark datasets: (i) bird calls, (ii) cat sounds, and (iii) the Environmental Sound Classification (ESC-50) database for identifying sources of noise in environments. To the best of our knowledge, this is the most extensive study investigating ensembles of CNNs for audio classification. The best-performing ensembles are compared and shown to either outperform or perform comparatively to the best methods reported in the literature on these datasets, including on the challenging ESC-50 dataset. We obtained a 97% accuracy on the bird dataset, 90.51% on the cat dataset, and 88.65% on ESC-50 using different approaches. In addition, the same ensemble model trained on the three datasets managed to reach the same results on the bird and cat datasets while losing only 0.1% on ESC-50. Thus, we have managed to create an off-the-shelf ensemble that can be trained on different datasets and reach performances competitive with the state of the art.

scholarly journals Environment Sound Classification System Based on Hybrid Feature and Convolutional Neural Network

2020 ◽  
Vol 38 (1) ◽  
pp. 162-169
Author(s):  
Ke Zhang ◽  
Yu Su ◽  
Jingyu Wang ◽  
Sanyu Wang ◽  
Yanhua Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Recognition System ◽  
Sound Recognition ◽  
Environmental Sound ◽  
Sound Classification ◽  
Proposed Model ◽  
Auditory Features

At present, the environment sound recognition system mainly identifies environment sounds with deep neural networks and a wide variety of auditory features. Therefore, it is necessary to analyze which auditory features are more suitable for deep neural networks based ESCR systems. In this paper, we chose three sound features which based on two widely used filters:the Mel and Gammatone filter banks. Subsequently, the hybrid feature MGCC is presented. Finally, a deep convolutional neural network is proposed to verify which features are more suitable for environment sound classification and recognition tasks. The experimental results show that the signal processing features are better than the spectrogram features in the deep neural network based environmental sound recognition system. Among all the acoustic features, the MGCC feature achieves the best performance than other features. Finally, the MGCC-CNN model proposed in this paper is compared with the state-of-the-art environmental sound classification models on the UrbanSound 8K dataset. The results show that the proposed model has the best classification accuracy.

scholarly journals An Optimum Tea Fermentation Detection Model Based on Deep Convolutional Neural Networks

Data ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 44
Author(s):  
Gibson Kimutai ◽  
Alexander Ngenzi ◽  
Rutabayiro Ngoga Said ◽  
Ambrose Kiprop ◽  
Anna Förster
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Deep Convolutional Neural Networks ◽  
Linear Discriminant ◽  
Learning Techniques ◽  
Classification Tasks

Tea is one of the most popular beverages in the world, and its processing involves a number of steps which includes fermentation. Tea fermentation is the most important step in determining the quality of tea. Currently, optimum fermentation of tea is detected by tasters using any of the following methods: monitoring change in color of tea as fermentation progresses and tasting and smelling the tea as fermentation progresses. These manual methods are not accurate. Consequently, they lead to a compromise in the quality of tea. This study proposes a deep learning model dubbed TeaNet based on Convolution Neural Networks (CNN). The input data to TeaNet are images from the tea Fermentation and Labelme datasets. We compared the performance of TeaNet with other standard machine learning techniques: Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Naive Bayes (NB). TeaNet was more superior in the classification tasks compared to the other machine learning techniques. However, we will confirm the stability of TeaNet in the classification tasks in our future studies when we deploy it in a tea factory in Kenya. The research also released a tea fermentation dataset that is available for use by the community.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

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