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2022-07-05
Hu, Zhibin, Yan, Chunman.  2021.  Lightweight Multi-Scale Network with Attention for Facial Expression Recognition. 2021 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). :695—698.
Aiming at the problems of the traditional convolutional neural network (CNN), such as too many parameters, single scale feature and inefficiency by some useless features, a lightweight multi-scale network with attention is proposed for facial expression recognition. The network uses the lightweight convolutional neural network model Xception and combines with the convolutional block attention module (CBAM) to learn key facial features; In addition, depthwise separable convolution module with convolution kernel of 3 × 3, 5 × 5 and 7 × 7 are used to extract features of facial expression image, and the features are fused to expand the receptive field and obtain more rich facial feature information. Experiments on facial expression datasets Fer2013 and KDEF show that the expression recognition accuracy is improved by 2.14% and 2.18% than the original Xception model, and the results further verify the effectiveness of our methods.
Siyaka, Hassan Opotu, Owolabi, Olumide, Bisallah, I. Hashim.  2021.  A New Facial Image Deviation Estimation and Image Selection Algorithm (Fide-Isa) for Facial Image Recognition Systems: The Mathematical Models. 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). :1—7.
Deep learning models have been successful and shown to perform better in terms of accuracy and efficiency for facial recognition applications. However, they require huge amount of data samples that were well annotated to be successful. Their data requirements have led to some complications which include increased processing demands of the systems where such systems were to be deployed. Reducing the training sample sizes of deep learning models is still an open problem. This paper proposes the reduction of the number of samples required by the convolutional neutral network used in training a facial recognition system using a new Facial Image Deviation Estimation and Image Selection Algorithm (FIDE-ISA). The algorithm was used to select appropriate facial image training samples incrementally based on their facial deviation. This will reduce the need for huge dataset in training deep learning models. Preliminary results indicated a 100% accuracy for models trained with 54 images (at least 3 images per individual) and above.
2022-06-30
Dankwa, Stephen, Yang, Lu.  2021.  An Optimal and Lightweight Convolutional Neural Network for Performance Evaluation in Smart Cities based on CAPTCHA Solving. 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). :1—6.
Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.
Cao, Yu.  2021.  Digital Character CAPTCHA Recognition Using Convolution Network. 2021 2nd International Conference on Computing and Data Science (CDS). :130—135.
Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a type of automatic program to determine whether the user is human or not. The most common type of CAPTCHA is a kind of message interpretation by twisting the letters and adding slight noises in the background, plays a role of verification code. In this paper, we will introduce the basis of Convolutional Neural Network first. Then based on the handwritten digit recognition using CNN, we will develop a network for CAPTCHA image recognition.
Kumar, Ashwani, Singh, Aditya Pratap.  2021.  Contour Based Deep Learning Engine to Solve CAPTCHA. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). 1:723—727.
A 'Completely Automated Public Turing test to tell Computers and Humans Apart' or better known as CAPTCHA is a image based test used to determine the authenticity of a user (ie. whether the user is human or not). In today's world, almost all the web services, such as online shopping sites, require users to solve CAPTCHAs that must be read and typed correctly. The challenge is that recognizing the CAPTCHAs is a relatively easy task for humans, but it is still hard to solve for computers. Ideally, a well-designed CAPTCHA should be solvable by humans at least 90% of the time, while programs using appropriate resources should succeed in less than 0.01% of the cases. In this paper, a deep neural network architecture is presented to extract text from CAPTCHA images on various platforms. The central theme of the paper is to develop an efficient & intelligent model that converts image-based CAPTCHA to text. We used convolutional neural network based architecture design instead of the traditional methods of CAPTCHA detection using image processing segmentation modules. The model consists of seven layers to efficiently correlate image features to the output character sequence. We tried a wide variety of configurations, including various loss and activation functions. We generated our own images database and the efficacy of our model was proven by the accuracy levels of 99.7%.
Zhou, Ziyue.  2021.  Digit Character CAPTCHA recognition Based on Deep Convolutional Neural Network. 2021 2nd International Conference on Computing and Data Science (CDS). :154—160.
With the developing of computer technology, Convolutional Neural Network (CNN) has made big development in both application region and research field. However, CAPTCHA (one Turing Test to tell difference between computer and human) technology is also widely used in many websites verification process and it has received great attention from researchers. In this essay, we introduced the CNN based on tensorflow framework and use the MINIST data set which is used in handwritten digit recognition to analyze the parameters and the structure of the CNN model. Moreover, we use different activation functions and compares them with different epochs. We also analyze many problems during the experiment to make the original data and the result more accurate.
2022-05-24
Leong Chien, Koh, Zainal, Anazida, Ghaleb, Fuad A., Nizam Kassim, Mohd.  2021.  Application of Knowledge-oriented Convolutional Neural Network For Causal Relation Extraction In South China Sea Conflict Issues. 2021 3rd International Cyber Resilience Conference (CRC). :1–7.
Online news articles are an important source of information for decisions makers to understand the causal relation of events that happened. However, understanding the causality of an event or between events by traditional machine learning-based techniques from natural language text is a challenging task due to the complexity of the language to be comprehended by the machines. In this study, the Knowledge-oriented convolutional neural network (K-CNN) technique is used to extract the causal relation from online news articles related to the South China Sea (SCS) dispute. The proposed K-CNN model contains a Knowledge-oriented channel that can capture the causal phrases of causal relationships. A Data-oriented channel that captures the position information was added to the K-CNN model in this phase. The online news articles were collected from the national news agency and then the sentences which contain relation such as causal, message-topic, and product-producer were extracted. Then, the extracted sentences were annotated and converted into lower form and base form followed by transformed into the vector by looking up the word embedding table. A word filter that contains causal keywords was generated and a K-CNN model was developed, trained, and tested using the collected data. Finally, different architectures of the K-CNN model were compared to find out the most suitable architecture for this study. From the study, it was found out that the most suitable architecture was the K-CNN model with a Knowledge-oriented channel and a Data-oriented channel with average pooling. This shows that the linguistic clues and the position features can improve the performance in extracting the causal relation from the SCS online news articles. Keywords-component; Convolutional Neural Network, Causal Relation Extraction, South China Sea.
2022-05-19
Zhang, Xiangyu, Yang, Jianfeng, Li, Xiumei, Liu, Minghao, Kang, Ruichun, Wang, Runmin.  2021.  Deeply Multi-channel guided Fusion Mechanism for Natural Scene Text Detection. 2021 7th International Conference on Big Data and Information Analytics (BigDIA). :149–156.
Scene text detection methods have developed greatly in the past few years. However, due to the limitation of the diversity of the text background of natural scene, the previous methods often failed when detecting more complicated text instances (e.g., super-long text and arbitrarily shaped text). In this paper, a text detection method based on multi -channel bounding box fusion is designed to address the problem. Firstly, the convolutional neural network is used as the basic network for feature extraction, including shallow text feature map and deep semantic text feature map. Secondly, the whole convolutional network is used for upsampling of feature map and fusion of feature map at each layer, so as to obtain pixel-level text and non-text classification results. Then, two independent text detection boxes channels are designed: the boundary box regression channel and get the bounding box directly on the score map channel. Finally, the result is obtained by combining multi-channel boundary box fusion mechanism with the detection box of the two channels. Experiments on ICDAR2013 and ICDAR2015 demonstrate that the proposed method achieves competitive results in scene text detection.
2022-05-06
Lee, Sang Hyun, Oh, Sang Won, Jo, Hye Seon, Na, Man Gyun.  2021.  Abnormality Diagnosis in NPP Using Artificial Intelligence Based on Image Data. 2021 5th International Conference on System Reliability and Safety (ICSRS). :103–107.
Accidents in Nuclear Power Plants (NPPs) can occur for a variety of causes. However, among these, the scale of accidents due to human error can be greater than expected. Accordingly, researches are being actively conducted using artificial intelligence to reduce human error. Most of the research shows high performance based on the numerical data on NPPs, but the expandability of researches using only numerical data is limited. Therefore, in this study, abnormal diagnosis was performed using artificial intelligence based on image data. The methods applied to abnormal diagnosis are the deep neural network, convolution neural network, and convolution recurrent neural network. Consequently, in nuclear power plants, it is expected that the application of more methodologies can be expanded not only in numerical data but also in image-based data.
2022-04-25
Ajoy, Atmik, Mahindrakar, Chethan U, Gowrish, Dhanya, A, Vinay.  2021.  DeepFake Detection using a frame based approach involving CNN. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). :1329–1333.
This paper proposes a novel model to detect Deep-Fakes, which are hyper-realistic fake videos generated by advanced AI algorithms involving facial superimposition. With a growing number of DeepFakes involving prominent political figures that hold a lot of social capital, their misuse can lead to drastic repercussions. These videos can not only be used to circulate false information causing harm to reputations of individuals, companies and countries, but also has the potential to cause civil unrest through mass hysteria. Hence it is of utmost importance to detect these DeepFakes and promptly curb their spread. We therefore propose a CNN-based model that learns inherently distinct patterns that change between a DeepFake and a real video. These distinct features include pixel distortion, inconsistencies with facial superimposition, skin colour differences, blurring and other visual artifacts. The proposed model has trained a CNN (Convolutional Neural Network), to effectively distinguish DeepFake videos using a frame-based approach based on aforementioned distinct features. Herein, the proposed work demonstrates the viability of our model in effectively identifying Deepfake faces in a given video source, so as to aid security applications employed by social-media platforms in credibly tackling the ever growing threat of Deepfakes, by effectively gauging the authenticity of videos, so that they may be flagged or ousted before they can cause irreparable harm.
Joseph, Zane, Nyirenda, Clement.  2021.  Deepfake Detection using a Two-Stream Capsule Network. 2021 IST-Africa Conference (IST-Africa). :1–8.
This paper aims to address the problem of Deepfake Detection using a Two-Stream Capsule Network. First we review methods used to create Deepfake content, as well as methods proposed in the literature to detect such Deepfake content. We then propose a novel architecture to detect Deepfakes, which consists of a two-stream Capsule network running in parallel that takes in both RGB images/frames as well as Error Level Analysis images. Results show that the proposed approach exhibits the detection accuracy of 73.39 % and 57.45 % for the Deepfake Detection Challenge (DFDC) and the Celeb-DF datasets respectively. These results are, however, from a preliminary implementation of the proposed approach. As part of future work, population-based optimization techniques such as Particle Swarm Optimization (PSO) will be used to tune the hyper parameters for better performance.
2022-04-22
Deng, Weimin, Xu, Da, Xu, Yuhan, Li, Mengshi.  2021.  Detection and Classification of Power Quality Disturbances Using Variational Mode Decomposition and Convolutional Neural Networks. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). :1514—1518.
Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.
2022-04-12
Ma, Haoyu, Cao, Jianqiu, Mi, Bo, Huang, Darong, Liu, Yang, Zhang, Zhenyuan.  2021.  Dark web traffic detection method based on deep learning. 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). :842—847.
Network traffic detection is closely related to network security, and it is also a hot research topic now. With the development of encryption technology, traffic detection has become more and more difficult, and many crimes have occurred on the dark web, so how to detect dark web traffic is the subject of this study. In this paper, we proposed a dark web traffic(Tor traffic) detection scheme based on deep learning and conducted experiments on public data sets. By analyzing the results of the experiment, our detection precision rate reached 95.47%.
2022-03-09
Chandankhede, Pankaj H., Titarmare, Abhijit S., Chauhvan, Sarang.  2021.  Voice Recognition Based Security System Using Convolutional Neural Network. 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). :738—743.
Following review depicts a unique speech recognition technique, based on planned analysis and utilization of Neural Network and Google API using speech’s characteristics. Multifactor security system pioneered for the authentication of vocal modalities and identification. Undergone project drives completely unique strategy of independent convolution layers structure and involvement of totally unique convolutions includes spectrum and Mel-frequency cepstral coefficient. This review takes in the statistical analysis of sound using scaled up and scaled down spectrograms, conjointly by exploitation the Google Speech-to-text API turns speech to pass code, it will be cross-verified for extended security purpose. Our study reveals that the incorporated methodology and the result provided elucidate the inclination of research in this area and encouraged us to advance in this field.
Kline, Timothy L..  2021.  Improving Domain Generalization in Segmentation Models with Neural Style Transfer. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI). :1324—1328.
Generalizing automated medical image segmentation methods to new image domains is inherently difficult. We have previously developed a number of automated segmentation methods that perform at the level of human readers on images acquired under similar conditions to the original training data. We are interested in exploring techniques that will improve model generalization to new imaging domains. In this study we explore a method to limit the inherent bias of these models to intensity and textural information. Using a dataset of 100 T2-weighted MR images with fat-saturation, and 100 T2-weighted MR images without fat-saturation, we explore the use of neural style transfer to induce shape preference and improve model performance on the task of segmenting the kidneys in patients affected by polycystic kidney disease. We find that using neural style transfer images improves the average dice value by 0.2. In addition, visualizing individual network kernel responses highlights a drastic difference in the optimized networks. Biasing models to invoke shape preference is a promising approach to create methods that are more closely aligned with human perception.
Park, Byung H., Chattopadhyay, Somrita, Burgin, John.  2021.  Haze Mitigation in High-Resolution Satellite Imagery Using Enhanced Style-Transfer Neural Network and Normalization Across Multiple GPUs. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. :2827—2830.
Despite recent advances in deep learning approaches, haze mitigation in large satellite images is still a challenging problem. Due to amorphous nature of haze, object detection or image segmentation approaches are not applicable. Also it is practically infeasible to obtain ground truths for training. Bounded memory capacity of GPUs is another constraint that limits the size of image to be processed. In this paper, we propose a style transfer based neural network approach to mitigate haze in a large overhead imagery. The network is trained without paired ground truths; further, perception loss is added to restore vivid colors, enhance contrast and minimize artifacts. The paper also illustrates our use of multiple GPUs in a collective way to produce a single coherent clear image where each GPU dehazes different portions of a large hazy image.
Gong, Peiyong, Zheng, Kai, Jiang, Yi, Liu, Jia.  2021.  Water Surface Object Detection Based on Neural Style Learning Algorithm. 2021 40th Chinese Control Conference (CCC). :8539—8543.
In order to detect the objects on the water surface, a neural style learning algorithm is proposed in this paper. The algorithm uses the Gram matrix of a pre-trained convolutional neural network to represent the style of the texture in the image, which is originally used for image style transfer. The objects on the water surface can be easily distinguished by the difference in their styles of the image texture. The algorithm is tested on the dataset of the Airbus Ship Detection Challenge on Kaggle. Compared to the other water surface object detection algorithms, the proposed algorithm has a good precision of 0.925 with recall equals to 0.86.
2022-03-08
Tian, Qian, Song, Qishun, Wang, Hongbo, Hu, Zhihong, Zhu, Siyu.  2021.  Verification Code Recognition Based on Convolutional Neural Network. 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). 4:1947—1950.

Verification code recognition system based on convolutional neural network. In order to strengthen the network security defense work, this paper proposes a novel verification code recognition system based on convolutional neural network. The system combines Internet technology and big data technology, combined with advanced captcha technology, can prevent hackers from brute force cracking behavior to a certain extent. In addition, the system combines convolutional neural network, which makes the verification code combine numbers and letters, which improves the complexity of the verification code and the security of the user account. Based on this, the system uses threshold segmentation method and projection positioning method to construct an 8-layer convolutional neural network model, which enhances the security of the verification code input link. The research results show that the system can enhance the complexity of captcha, improve the recognition rate of captcha, and improve the security of user accounting.

2022-03-01
Li, Xiaojian, Chen, Jing, Jiang, Yiyi, Hu, Hangping, Yang, Haopeng.  2021.  An Accountability-Oriented Generation approach to Time-Varying Structure of Cloud Service. 2021 IEEE International Conference on Services Computing (SCC). :413–418.
In the current cloud service development, during the widely used of cloud service, it can self organize and respond on demand when the cloud service in phenomenon of failure or violation, but it may still cause violation. The first step in forecasting or accountability for this situation, is to generate a dynamic structure of cloud services in a timely manner. In this research, it has presented a method to generate the time-varying structure of cloud service. Firstly, dependencies between tasks and even instances within a job of cloud service are visualized to explore the time-varying characteristics contained in the cloud service structure. And then, those dependencies are discovered quantitatively using CNN (Convolutional Neural Networks). Finally, it structured into an event network of cloud service for tracing violation and other usages. A validation to this approach has been examined by an experiment based on Alibaba’s dataset. A function integrity of this approach may up to 0.80, which is higher than Bai Y and others which is no more than 0.60.
Wang, Weidong, Zheng, Yufu, Bao, Yeling, Shui, Shengkun, Jiang, Tao.  2021.  Modulated Signal Recognition Based on Feature-Multiplexed Convolutional Neural Networks. 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). 2:621–624.
Modulated signal identification plays a crucial role in both military reconnaissance and civilian signal regulation. Traditionally, modulated signal identification is based on high-order statistics, but this approach has many drawbacks. With the development of deep learning, its advantages are fully exploited by combining it with modulated signals to avoid the complex process of computing a priori knowledge while having good fault tolerance. In this paper, ten digital modulated signals are classified and recognized, and improvements are made on the basis of convolutional neural networks, using feature reuse to increase the depth of the convolutional layer and extract signal features with better results. After experimental analysis, the recognition accuracy increases with the rise of the signal-to-noise ratio, and can reach 90% and above when the signal-to-noise ratio is 30dB.
Li, Pei, Wang, Longlong.  2021.  Combined Neural Network Based on Deep Learning for AMR. 2021 7th International Conference on Computer and Communications (ICCC). :1244–1248.
Automatic modulation recognition (AMR) plays an important role in cognitive radio and electronic reconnaissance applications. In order to solve the problem that the lack of modulation signal data sets, the labeled data sets are generated by the software radio equipment NI-USRP 2920 and LabVIEW software development tool. In this paper, a combined network based on deep learning is proposed to identify ten types of digital modulation signals. Convolutional neural network (CNN) and Inception network are trained on different data sets, respectively. We combine CNN with Inception network to distinguish different modulation signals well. Experimental results show that our proposed method can recognize ten types of digital modulation signals with high identification accuracy, even in scenarios with a low signal-to-noise ratio (SNR).
2022-02-07
Yang, Chen, Yang, Zepeng, Hou, Jia, Su, Yang.  2021.  A Lightweight Full Homomorphic Encryption Scheme on Fully-connected Layer for CNN Hardware Accelerator achieving Security Inference. 2021 28th IEEE International Conference on Electronics, Circuits, and Systems (ICECS). :1–4.
The inference results of neural network accelerators often involve personal privacy or business secrets in intelligent systems. It is important for the safety of convolutional neural network (CNN) accelerator to prevent the key data and inference result from being leaked. The latest CNN models have started to combine with fully homomorphic encryption (FHE), ensuring the data security. However, the computational complexity, data storage overhead, inference time are significantly increased compared with the traditional neural network models. This paper proposed a lightweight FHE scheme on fully-connected layer for CNN hardware accelerator to achieve security inference, which not only protects the privacy of inference results, but also avoids excessive hardware overhead and great performance degradation. Compared with state-of-the-art works, this work reduces computational complexity by approximately 90% and decreases ciphertext size by 87%∼95%.
2022-01-25
Sun, Hao, Xu, Yanjie, Kuang, Gangyao, Chen, Jin.  2021.  Adversarial Robustness Evaluation of Deep Convolutional Neural Network Based SAR ATR Algorithm. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. :5263–5266.
Robustness, both to accident and to malevolent perturbations, is a crucial determinant of the successful deployment of deep convolutional neural network based SAR ATR systems in various security-sensitive applications. This paper performs a detailed adversarial robustness evaluation of deep convolutional neural network based SAR ATR models across two public available SAR target recognition datasets. For each model, seven different adversarial perturbations, ranging from gradient based optimization to self-supervised feature distortion, are generated for each testing image. Besides adversarial average recognition accuracy, feature attribution techniques have also been adopted to analyze the feature diffusion effect of adversarial attacks, which promotes the understanding of vulnerability of deep learning models.
2022-01-10
Sallam, Youssef F., Ahmed, Hossam El-din H., Saleeb, Adel, El-Bahnasawy, Nirmeen A., El-Samie, Fathi E. Abd.  2021.  Implementation of Network Attack Detection Using Convolutional Neural Network. 2021 International Conference on Electronic Engineering (ICEEM). :1–6.
The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Probe). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtrain+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.02 wrong alert rates for the two test scenarios, respectively.
Agarwal, Shivam, Khatter, Kiran, Relan, Devanjali.  2021.  Security Threat Sounds Classification Using Neural Network. 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom). :690–694.
Sound plays a key role in human life and therefore sound recognition system has a great future ahead. Sound classification and identification system has many applications such as system for personal security, critical surveillance, etc. The main aim of this paper is to detect and classify the security sound event using the surveillance camera systems with integrated microphone based on the generated spectrograms of the sounds. This will enable to track security events in cases of emergencies. The goal is to propose a security system to accurately detect sound events and make a better security sound event detection system. We propose to use a convolutional neural network (CNN) to design the security sound detection system to detect a security event with minimal sound. We used the spectrogram images to train the CNN. The neural network was trained using different security sounds data which was then used to detect security sound events during testing phase. We used two datasets for our experiment training and testing datasets. Both the datasets contain 3 different sound events (glass break, gun shots and smoke alarms) to train and test the model, respectively. The proposed system yields the good accuracy for the sound event detection even with minimum available sound data. The designed system achieved accuracy was 92% and 90% using CNN on training dataset and testing dataset. We conclude that the proposed sound classification framework which using the spectrogram images of sounds can be used efficiently to develop the sound classification and recognition systems.