Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687006
Chenguang Xu
The purposed of hyperspectral unmixing is to estimate the spectral signatures composing the data (endmembers) and their abundance fractions. However, most of the traditional sparse unmixing methods are effective in the case of high signal-to-noise ratio (SNR), but is not good in the case of high noise. In order to solve this problem, we innovatively integrates adaptive total variation (ATV) regularization into hyperspectral sparse unmixing and propose a new hyperspectal sparse unmixing model named adaptive total variation regularized for sparse unmixing (SU_ATV). The model can adaptively adjust the horizontal difference and vertical difference of TV, can better optimize the efficiency of TV to improve the anti-noise performance. The experimental results show that SU_ATV has good anti-noise performance to the sparse unmixing.
{"title":"Adaptive Total Variation Regularized for Hyperspectral Unmixing","authors":"Chenguang Xu","doi":"10.1109/PIC53636.2021.9687006","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687006","url":null,"abstract":"The purposed of hyperspectral unmixing is to estimate the spectral signatures composing the data (endmembers) and their abundance fractions. However, most of the traditional sparse unmixing methods are effective in the case of high signal-to-noise ratio (SNR), but is not good in the case of high noise. In order to solve this problem, we innovatively integrates adaptive total variation (ATV) regularization into hyperspectral sparse unmixing and propose a new hyperspectal sparse unmixing model named adaptive total variation regularized for sparse unmixing (SU_ATV). The model can adaptively adjust the horizontal difference and vertical difference of TV, can better optimize the efficiency of TV to improve the anti-noise performance. The experimental results show that SU_ATV has good anti-noise performance to the sparse unmixing.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687009
Jian Dong, Li Zhang, Zilong Liu, Zhiwei Lin, Zhiming Cai
As it is difficult to classify and identify the actions caused by the distortion of radar signal during acquisition process, this paper obtains the feature value of action signal through preprocessing such as abnormal point removal and wavelet filtering, and obtains the signal fluctuation section of action through short-term power spectral density. In the eight classification experiment and the nine classification experiment, the accuracies of traditional Bayesian network, BP network and support vector machine (SVM) are no higher than 90.0% For the test set with too small samples and some distortion, even using GWO-SVM, the recognition rate is still less than 90%. Therefore, this paper improves the wolf swarm position vector in GWO algorithm, and optimizes the penalty function and function radius in SVM model. The experimental results of our method show that the accuracies of eight classification and nine classification experiments are 92.4% and 90.4% respectively, which are better than those of SVM and GWO-SVM.
{"title":"An Action Recognition Method Based on Radar Signal with Improved GWO-SVM Algorithm","authors":"Jian Dong, Li Zhang, Zilong Liu, Zhiwei Lin, Zhiming Cai","doi":"10.1109/PIC53636.2021.9687009","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687009","url":null,"abstract":"As it is difficult to classify and identify the actions caused by the distortion of radar signal during acquisition process, this paper obtains the feature value of action signal through preprocessing such as abnormal point removal and wavelet filtering, and obtains the signal fluctuation section of action through short-term power spectral density. In the eight classification experiment and the nine classification experiment, the accuracies of traditional Bayesian network, BP network and support vector machine (SVM) are no higher than 90.0% For the test set with too small samples and some distortion, even using GWO-SVM, the recognition rate is still less than 90%. Therefore, this paper improves the wolf swarm position vector in GWO algorithm, and optimizes the penalty function and function radius in SVM model. The experimental results of our method show that the accuracies of eight classification and nine classification experiments are 92.4% and 90.4% respectively, which are better than those of SVM and GWO-SVM.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130927693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blind guidance system has always been a research hotspot for years. Although there are many kinds of blind guidance systems on the market, most of them prompt from the perspective of a single sense of tactile or auditory. The blind guidance method of single sense can be unstable and it does not fully mobilize other general senses of the with vision impairment. This paper designs and implements a multi-sensory blind guidance system that provides tactile and auditory sensations by using the ORB-SLAM and YOLO techniques. Based on the RGB-D camera, the local obstacle avoidance system is realized at the tactile level through the point cloud filtering that feedback the results to the user through vibrating motors. The improved ORB-SLAM can generate a dense navigation map to implement a global obstacle avoidance system through the coordinate transformation. Real-time target detection and the YOLO-based prompt voice system is implemented at the auditory level. The system can detect the specific category and give the location of obstacles as real-time voice messages. The functions mentioned above are integrated and verified as a smart cane. Experimental results show that the position and category of the obstacles in the surrounding environment can be detected accurately in real-time through our system. By combining YOLO and ORB- SLAM, we can provide a piece of useful auxiliary equipment to the community of vision impairment and enable users to move about safely.
{"title":"A Multi-Sensory Blind Guidance System Based on YOLO and ORB-SLAM","authors":"Chufan Rui, Yichen Liu, Junru Shen, Zhaobin Li, Zaipeng Xie","doi":"10.1109/PIC53636.2021.9687018","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687018","url":null,"abstract":"Blind guidance system has always been a research hotspot for years. Although there are many kinds of blind guidance systems on the market, most of them prompt from the perspective of a single sense of tactile or auditory. The blind guidance method of single sense can be unstable and it does not fully mobilize other general senses of the with vision impairment. This paper designs and implements a multi-sensory blind guidance system that provides tactile and auditory sensations by using the ORB-SLAM and YOLO techniques. Based on the RGB-D camera, the local obstacle avoidance system is realized at the tactile level through the point cloud filtering that feedback the results to the user through vibrating motors. The improved ORB-SLAM can generate a dense navigation map to implement a global obstacle avoidance system through the coordinate transformation. Real-time target detection and the YOLO-based prompt voice system is implemented at the auditory level. The system can detect the specific category and give the location of obstacles as real-time voice messages. The functions mentioned above are integrated and verified as a smart cane. Experimental results show that the position and category of the obstacles in the surrounding environment can be detected accurately in real-time through our system. By combining YOLO and ORB- SLAM, we can provide a piece of useful auxiliary equipment to the community of vision impairment and enable users to move about safely.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127892733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687090
Jianshe Liu
The existing water cannons are basically open-loop control, which can not provide real-time feedback on the position difference between the shooting flow point and the target, especially for the moving target, there is a large strike error. The jet closed-loop control technology is an important way to realize the accurate and continuous strike of water cannon on target. Considering only the influence of gravity and air resistance, a jet closed -loop control method is proposed. In this method, the horizontal Angle and pitch Angle of the water cannon are adjusted by geometric calculation and the jet motion trajectory model are established respectively. On the basis of image processing, a method to adjust the Angle of the water cannon again is designed, and the feasibility of this method is verified by a lot of simulation experiments. Experiments show that this method can dynamically adjust the jet Angle in real time according to the target bearing, and has high accuracy and real time.
{"title":"The Jet Closed-Loop Control Method Based on Image Processing","authors":"Jianshe Liu","doi":"10.1109/PIC53636.2021.9687090","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687090","url":null,"abstract":"The existing water cannons are basically open-loop control, which can not provide real-time feedback on the position difference between the shooting flow point and the target, especially for the moving target, there is a large strike error. The jet closed-loop control technology is an important way to realize the accurate and continuous strike of water cannon on target. Considering only the influence of gravity and air resistance, a jet closed -loop control method is proposed. In this method, the horizontal Angle and pitch Angle of the water cannon are adjusted by geometric calculation and the jet motion trajectory model are established respectively. On the basis of image processing, a method to adjust the Angle of the water cannon again is designed, and the feasibility of this method is verified by a lot of simulation experiments. Experiments show that this method can dynamically adjust the jet Angle in real time according to the target bearing, and has high accuracy and real time.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687084
Chunzhi Hou, Jiarui Shi, Baohang Zhang
In recent years, the role of a single dendritic neural structures with non-linear localisation in computing has attracted a lot of attention from the industry. The dendritic neuron model (DNM) is an approximate logical neuron model based on dendrites, with branches of dendrites corresponding to three distributions in coordinates.The model is trained to assort data as needed by mimicking the mechanisms of transmitting information and biological nerves. Traditionally DNM models use error back propagation (BP) to optimise local minimum problems, but also degrade their performance. We now train it using an equilibrium optimizer based on physical phenomena inspired by control volume mass balance. Experimental results due to some real-world classification problems show that the mentioned algorithm can improve the accuracy of the DNM solution.
{"title":"Evolving Dendritic Neuron Model by Equilibrium Optimizer Algorithm","authors":"Chunzhi Hou, Jiarui Shi, Baohang Zhang","doi":"10.1109/PIC53636.2021.9687084","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687084","url":null,"abstract":"In recent years, the role of a single dendritic neural structures with non-linear localisation in computing has attracted a lot of attention from the industry. The dendritic neuron model (DNM) is an approximate logical neuron model based on dendrites, with branches of dendrites corresponding to three distributions in coordinates.The model is trained to assort data as needed by mimicking the mechanisms of transmitting information and biological nerves. Traditionally DNM models use error back propagation (BP) to optimise local minimum problems, but also degrade their performance. We now train it using an equilibrium optimizer based on physical phenomena inspired by control volume mass balance. Experimental results due to some real-world classification problems show that the mentioned algorithm can improve the accuracy of the DNM solution.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115856448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687035
Kaiyu Dai, Yiyang Qiu, Rui Zhang
With the deepening integration of artificial intelligence, ICT in education is approaching to the stage of smart education, the main purpose of which is to realize learning personalization. This paper constructs an intelligent tutoring system to allow teacher establish the course knowledge model visually based on ontology. This system evaluates the learning situation of students using a test auto-generated by a global prediction accuracy optimization algorithm. The learning diagnosis module is implemented according to the learning situations of students and the structure analysis of knowledge graph based on node contribution. The resource recommendation module is implemented through the importance ranking of learning resources. The prototype system is constructed and the experiments are conducted. The results show that our approach can achieve personalized learning well in a certain range.
{"title":"The Construction of Learning Diagnosis and Resources Recommendation System Based on Knowledge Graph","authors":"Kaiyu Dai, Yiyang Qiu, Rui Zhang","doi":"10.1109/PIC53636.2021.9687035","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687035","url":null,"abstract":"With the deepening integration of artificial intelligence, ICT in education is approaching to the stage of smart education, the main purpose of which is to realize learning personalization. This paper constructs an intelligent tutoring system to allow teacher establish the course knowledge model visually based on ontology. This system evaluates the learning situation of students using a test auto-generated by a global prediction accuracy optimization algorithm. The learning diagnosis module is implemented according to the learning situations of students and the structure analysis of knowledge graph based on node contribution. The resource recommendation module is implemented through the importance ranking of learning resources. The prototype system is constructed and the experiments are conducted. The results show that our approach can achieve personalized learning well in a certain range.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"54 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113970618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Music emotion recognition (MER) has attracted much interest in the past decades for efficient music information organization and retrieval. Although deep learning has been applied to this field to avoid facing the complexity of feature engineering, the processing of original information within music pieces has become another challenge. In this paper, we propose a novel method named Frequency Embedded Regularization Network (FERN) for continuous MER to overcome this issue. Specifically, we apply regularized ResNet to automatically extract features through spectrograms with embedded frequency channels. The receptive fields in the deep architecture are adjusted by modifying the kernel size to maintain original information completely. Furthermore, Long Short-Term Memory (LSTM) is employed to learn the sequential relationship from the extracted contextual features. We conduct experiments on the benchmark dataset 1000 Songs. The experimental results show that our method is superior to most of the compared methods in terms of extracting salient features and catching the distribution of emotions within music pieces.
{"title":"Frequency Embedded Regularization Network for Continuous Music Emotion Recognition","authors":"Meixian Zhang, Yonghua Zhu, Ning Ge, Yunwen Zhu, Tianyu Feng, Wenjun Zhang","doi":"10.1109/PIC53636.2021.9687003","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687003","url":null,"abstract":"Music emotion recognition (MER) has attracted much interest in the past decades for efficient music information organization and retrieval. Although deep learning has been applied to this field to avoid facing the complexity of feature engineering, the processing of original information within music pieces has become another challenge. In this paper, we propose a novel method named Frequency Embedded Regularization Network (FERN) for continuous MER to overcome this issue. Specifically, we apply regularized ResNet to automatically extract features through spectrograms with embedded frequency channels. The receptive fields in the deep architecture are adjusted by modifying the kernel size to maintain original information completely. Furthermore, Long Short-Term Memory (LSTM) is employed to learn the sequential relationship from the extracted contextual features. We conduct experiments on the benchmark dataset 1000 Songs. The experimental results show that our method is superior to most of the compared methods in terms of extracting salient features and catching the distribution of emotions within music pieces.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133749109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687034
Mulugeta Weldezgina Asres, G. Cummings, P. Parygin, A. Khukhunaishvili, M. Toms, A. Campbell, S. Cooper, D. Yu, J. Dittmann, C. Omlin
The ever-increasing detector complexity at CERN triggers a call for an increasing level of automation. Since the quality of collected physics data hinges on the quality of the detector components at the time of data-taking, the rapid identification and resolution of detector system anomalies will result in a better amount of high-quality particle data. Therefore, this study proposes CGVAE, a data-driven unsupervised anomaly detection using a deep learning model, for detector system monitoring from multivariate time series sensor data. The CGVAE model is composed of a variational autoencoder with convolutional and gated recurrent unit networks for fast localized feature extraction, long temporal characteristics capturing, and descriptive representation learning. Furthermore, to mitigate signal reconstruction overfitting on anomalous patterns, the CGVAE employs encoded latent feature- and reconstruction-based metrics for anomaly detection. Moreover, the model integrates feature attribution algorithms to explain the contribution of the input sensors to the detected anomalies. The experimental evaluation on large sensor data sets of the Hadron Calorimeter of the CMS experiment demonstrates the efficacy of the proposed model in capturing temporal anomalies.
{"title":"Unsupervised Deep Variational Model for Multivariate Sensor Anomaly Detection","authors":"Mulugeta Weldezgina Asres, G. Cummings, P. Parygin, A. Khukhunaishvili, M. Toms, A. Campbell, S. Cooper, D. Yu, J. Dittmann, C. Omlin","doi":"10.1109/PIC53636.2021.9687034","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687034","url":null,"abstract":"The ever-increasing detector complexity at CERN triggers a call for an increasing level of automation. Since the quality of collected physics data hinges on the quality of the detector components at the time of data-taking, the rapid identification and resolution of detector system anomalies will result in a better amount of high-quality particle data. Therefore, this study proposes CGVAE, a data-driven unsupervised anomaly detection using a deep learning model, for detector system monitoring from multivariate time series sensor data. The CGVAE model is composed of a variational autoencoder with convolutional and gated recurrent unit networks for fast localized feature extraction, long temporal characteristics capturing, and descriptive representation learning. Furthermore, to mitigate signal reconstruction overfitting on anomalous patterns, the CGVAE employs encoded latent feature- and reconstruction-based metrics for anomaly detection. Moreover, the model integrates feature attribution algorithms to explain the contribution of the input sensors to the detected anomalies. The experimental evaluation on large sensor data sets of the Hadron Calorimeter of the CMS experiment demonstrates the efficacy of the proposed model in capturing temporal anomalies.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1995 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125554148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687049
Yimin Peng, Rong Hu, Yiping Wen
In the big data environment, the sparsity problem of collaborative filtering recommendation algorithm becomes increasingly serious, which has a great impact on the accuracy of recommendation. In some recent researches, item categories were input into neural networks to enrich the embedded information in the process of training. However, these methods generally simultaneously use item categories and items as embedded information, which may weaken the importance of item categories. Therefore, this paper proposes a neural collaborative filtering method based on category assistance. In this method, the interaction between item category and user is first modeled by Neural Matrix Factorization ((Neu-MF)), which raises the impact of item category in the relationship extraction between items and users. Then, only the items in the trained results of categories are used in an optimized Neural Collaborative Filtering (NCF) framework for item recommendation. Based on the real ecommerce data set from Alibaba, experimental results show that this method obtains better result in the Hit Rate (HR) and the Normalized Discounted Cumulative Gain (NDCG) compared with other baseline methods.
{"title":"CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation","authors":"Yimin Peng, Rong Hu, Yiping Wen","doi":"10.1109/PIC53636.2021.9687049","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687049","url":null,"abstract":"In the big data environment, the sparsity problem of collaborative filtering recommendation algorithm becomes increasingly serious, which has a great impact on the accuracy of recommendation. In some recent researches, item categories were input into neural networks to enrich the embedded information in the process of training. However, these methods generally simultaneously use item categories and items as embedded information, which may weaken the importance of item categories. Therefore, this paper proposes a neural collaborative filtering method based on category assistance. In this method, the interaction between item category and user is first modeled by Neural Matrix Factorization ((Neu-MF)), which raises the impact of item category in the relationship extraction between items and users. Then, only the items in the trained results of categories are used in an optimized Neural Collaborative Filtering (NCF) framework for item recommendation. Based on the real ecommerce data set from Alibaba, experimental results show that this method obtains better result in the Hit Rate (HR) and the Normalized Discounted Cumulative Gain (NDCG) compared with other baseline methods.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687058
Maxime Goubeaud, Nicolla Gmyrek, Farzin Ghorban, Lucas Schelkes, A. Kummert
In machine learning, data augmentation is commonly used to generate synthetic samples in order to augment datasets used to train models. The motivation behind data augmentation is to reduce the error-rate of models by increasing the diversity in the dataset. In this paper, we present a new data augmentation method for spectrograms of time series that we name Random Noise Boxes. Random Noise Boxes works by multiplying each spectrogram in a dataset with a predefined number of identical spectrograms and thereafter replacing randomly chosen square-sized parts of the resulting spectrograms with boxes of random noise pixels. We demonstrate the effectiveness of the proposed method by conducting experiments using differentsized CNN classifiers evaluated on nine well-known datasets from the UCR Time Series Classification Archive. We show that our method is beneficial in most cases, as we observe an increase of accuracy and F1-Score on most datasets.
{"title":"Random Noise Boxes: Data Augmentation for Spectrograms","authors":"Maxime Goubeaud, Nicolla Gmyrek, Farzin Ghorban, Lucas Schelkes, A. Kummert","doi":"10.1109/PIC53636.2021.9687058","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687058","url":null,"abstract":"In machine learning, data augmentation is commonly used to generate synthetic samples in order to augment datasets used to train models. The motivation behind data augmentation is to reduce the error-rate of models by increasing the diversity in the dataset. In this paper, we present a new data augmentation method for spectrograms of time series that we name Random Noise Boxes. Random Noise Boxes works by multiplying each spectrogram in a dataset with a predefined number of identical spectrograms and thereafter replacing randomly chosen square-sized parts of the resulting spectrograms with boxes of random noise pixels. We demonstrate the effectiveness of the proposed method by conducting experiments using differentsized CNN classifiers evaluated on nine well-known datasets from the UCR Time Series Classification Archive. We show that our method is beneficial in most cases, as we observe an increase of accuracy and F1-Score on most datasets.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129139164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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