Pub Date : 2020-10-05DOI: 10.1109/SIU49456.2020.9302210
Elif Sanlialp, M. A. Bülbül
The usage of social media is increasing day by day. People use social media platforms to communicate with their friends or other users and to demonstrate what they are interested in by sharing different kinds of media such as photos, texts, and videos. A portion of the posted content also include location information. Such posts having location information are called geo-tagged posts in social networks. According to the analysis of geo-tagged posts, popular locations or activities can be identified. This study proposes a method to identify the most representative subset of the visual content shared in a region through social media. Our approach aims to detect the popular places and events and utilizes Scale-Invariant Feature Transform (SIFT) features. Identified representative visuals are used to generate a web based tourist map. In this study, Flickr is used as the source of geotagged visual content.
{"title":"Geolocated Visual Summarization of Social Media Data","authors":"Elif Sanlialp, M. A. Bülbül","doi":"10.1109/SIU49456.2020.9302210","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302210","url":null,"abstract":"The usage of social media is increasing day by day. People use social media platforms to communicate with their friends or other users and to demonstrate what they are interested in by sharing different kinds of media such as photos, texts, and videos. A portion of the posted content also include location information. Such posts having location information are called geo-tagged posts in social networks. According to the analysis of geo-tagged posts, popular locations or activities can be identified. This study proposes a method to identify the most representative subset of the visual content shared in a region through social media. Our approach aims to detect the popular places and events and utilizes Scale-Invariant Feature Transform (SIFT) features. Identified representative visuals are used to generate a web based tourist map. In this study, Flickr is used as the source of geotagged visual content.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125569265","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302409
F. Nassehi, Mertcan Özdemir, O. Eroğul
Mathematical difficulty in mathematics, also known as Dyscalculia, is a learning disability that makes it difficult to comprehend numbers and symbols and to perform mathematical calculations. Generally, it's known as the mathematical version of dyslexia. In this study, Hjorth parameters were used to analyze the change of EEG signals during mental arithmetic task for multifractal analysis of EEG signals. Other features such as relative power and statistical properties were also analyzed. This study may provide an alternative diagnostic method for some psychological disorders such as arithmetic learning and difficulties in understanding, and mathematical difficulties. The aim of the study is to provide a simple test and feature extraction for the diagnosis of mental illnesses and mathematics difficulties at an early age. For this purpose, the data are taken from the same number of participants who do good and bad at math problems. We found that ratio of theta band's amplitudes on alpha band's amplitude could be a main feature to investigate the mathematical difficulties.
{"title":"Investigating EEG Based Marker for Diagnosis of Mathematical Difficulties","authors":"F. Nassehi, Mertcan Özdemir, O. Eroğul","doi":"10.1109/SIU49456.2020.9302409","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302409","url":null,"abstract":"Mathematical difficulty in mathematics, also known as Dyscalculia, is a learning disability that makes it difficult to comprehend numbers and symbols and to perform mathematical calculations. Generally, it's known as the mathematical version of dyslexia. In this study, Hjorth parameters were used to analyze the change of EEG signals during mental arithmetic task for multifractal analysis of EEG signals. Other features such as relative power and statistical properties were also analyzed. This study may provide an alternative diagnostic method for some psychological disorders such as arithmetic learning and difficulties in understanding, and mathematical difficulties. The aim of the study is to provide a simple test and feature extraction for the diagnosis of mental illnesses and mathematics difficulties at an early age. For this purpose, the data are taken from the same number of participants who do good and bad at math problems. We found that ratio of theta band's amplitudes on alpha band's amplitude could be a main feature to investigate the mathematical difficulties.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125606468","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302517
Hasan İhsan Turhan, Duygu Acar, Nuri Baran Ayana, Kenan Ahiska, M. Demirekler
In this study, a novel methodology is proposed for more accurate target tracking. The proposed method is basically built on the merging of interacting multiple model (IMM) structures using Hidden Markov Model (HMM). Thus, more models are used than the ordinary IMM algorithm, but more accurate state vectors are estimated by selecting the most likely ones. The proposed algorithm is compared with the variable structure IMM (VSIMM) algorithm, which is the most similar methodology in the literature, in MATLAB environment and the results are presented.
{"title":"Hidden Markov Model Based Variable Structured Multiple Model Algorithm","authors":"Hasan İhsan Turhan, Duygu Acar, Nuri Baran Ayana, Kenan Ahiska, M. Demirekler","doi":"10.1109/SIU49456.2020.9302517","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302517","url":null,"abstract":"In this study, a novel methodology is proposed for more accurate target tracking. The proposed method is basically built on the merging of interacting multiple model (IMM) structures using Hidden Markov Model (HMM). Thus, more models are used than the ordinary IMM algorithm, but more accurate state vectors are estimated by selecting the most likely ones. The proposed algorithm is compared with the variable structure IMM (VSIMM) algorithm, which is the most similar methodology in the literature, in MATLAB environment and the results are presented.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123407474","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302348
Feridun Tutuncuoglu, S. Gezici
In OFDM-based wireless communication systems, carrier frequency offset (CFO) has been one of the important factors that impair the communication performance. Carriers placed as orthogonal to each other lose their orthogonality by being affected from frequency shifts due to differences in crystals at the receiver and the transmitter or due to channel effects. For this reason, estimators are used to determine carrier frequency differences. To this aim, CFO is estimated using pilot signals placed in an OFDM block. In this work, the Cramer-Rao lower bound (CRLB) is derived for CFO estimation based on the tracking reference signal (TRS) used in 5G standards. In addition, maximum likelihood and least squares based CFO estimators are investigated for the TRS proposed in the literature and 3GPP contribution reports. Finally, by using these estimators, a new estimator with enhanced performance is proposed. Keywords—Carrier frequency offset, estimator, tracking reference signal, 5G
{"title":"Carrier Frequency Offset Estimation Based on Tracking Reference Signal in 5G","authors":"Feridun Tutuncuoglu, S. Gezici","doi":"10.1109/SIU49456.2020.9302348","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302348","url":null,"abstract":"In OFDM-based wireless communication systems, carrier frequency offset (CFO) has been one of the important factors that impair the communication performance. Carriers placed as orthogonal to each other lose their orthogonality by being affected from frequency shifts due to differences in crystals at the receiver and the transmitter or due to channel effects. For this reason, estimators are used to determine carrier frequency differences. To this aim, CFO is estimated using pilot signals placed in an OFDM block. In this work, the Cramer-Rao lower bound (CRLB) is derived for CFO estimation based on the tracking reference signal (TRS) used in 5G standards. In addition, maximum likelihood and least squares based CFO estimators are investigated for the TRS proposed in the literature and 3GPP contribution reports. Finally, by using these estimators, a new estimator with enhanced performance is proposed. Keywords—Carrier frequency offset, estimator, tracking reference signal, 5G","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126221343","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302343
Enver Fakhan, E. Arisoy
In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data. Keywords—Acoustic model adaptation, automatic speech recognition, artificial neural networks
{"title":"Domain Adaptation Approaches for Acoustic Modeling","authors":"Enver Fakhan, E. Arisoy","doi":"10.1109/SIU49456.2020.9302343","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302343","url":null,"abstract":"In the recent years, with the development of neural network based models, ASR systems have achieved a tremendous performance increase. However, this performance increase mostly depends on the amount of training data and the computational power. In a low-resource data scenario, publicly available datasets can be utilized to overcome data scarcity. Furthermore, using a pre-trained model and adapting it to the in-domain data can help with computational constraint. In this paper we have leveraged two different publicly available datasets and investigate various acoustic model adaptation approaches. We show that 4% word error rate can be achieved using a very limited in-domain data. Keywords—Acoustic model adaptation, automatic speech recognition, artificial neural networks","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126419412","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302205
Abdullah Ammar Karcioglu, Ahmet Cahit Yaşa
Automatic text summarization is one of the applications of natural language processing that has been studied for a long time. The increase in the amount of information in web resources has increased the need for automatic text summarization methods. It is difficult to design a system to produce abstracts created by human hands. For this reason, many researchers have focused on extracting sentences or paragraphs, which is a kind of summary. In this study, we introduce a method that was created using genetic algorithms to generate such summaries. After the texts are preprocessed, vocabulary is created and given as input to the proposed method. The sentence selection based on Genetic Algorithm is used to summarize and after that the summary is created, it is evaluated using the fitness function. In our first model, the fitness function is based on the frequency of each word and the word pair frequencies. The results of the applied model are discussed using the same dataset in another method based on tf-idf, with precision, recall, fscore and Rouge metrics.
{"title":"Automatic Summary Extraction in Texts Using Genetic Algorithms","authors":"Abdullah Ammar Karcioglu, Ahmet Cahit Yaşa","doi":"10.1109/SIU49456.2020.9302205","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302205","url":null,"abstract":"Automatic text summarization is one of the applications of natural language processing that has been studied for a long time. The increase in the amount of information in web resources has increased the need for automatic text summarization methods. It is difficult to design a system to produce abstracts created by human hands. For this reason, many researchers have focused on extracting sentences or paragraphs, which is a kind of summary. In this study, we introduce a method that was created using genetic algorithms to generate such summaries. After the texts are preprocessed, vocabulary is created and given as input to the proposed method. The sentence selection based on Genetic Algorithm is used to summarize and after that the summary is created, it is evaluated using the fitness function. In our first model, the fitness function is based on the frequency of each word and the word pair frequencies. The results of the applied model are discussed using the same dataset in another method based on tf-idf, with precision, recall, fscore and Rouge metrics.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126438806","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302491
A. Malik, Sezin Barın, M. E. Yüksel
Heart sound (HS) signals contain valuable diagnostic information for detection of heart abnormalities. The early detection of heart abnormalities plays an important role in reducing the mortality rate caused by heart diseases. Auscultation, the process of listening to heart sounds, is the first diagnostic method of heart diseases. This process is highly dependent on the physician expertise, making the diagnosis more of a subjective issue. There is ongoing research to automate heart sound diagnosis. Advances in machine learning have provided an easier, cheaper and objective diagnosis of diseases. Algorithms developed for heart sound classifications rely on several features and the accuracy of a model depends on the feature vector. The advent of deep learning (DL) provides a possible solution to overcome the overwhelming and time-consuming step of feature extraction. Convolutional neural networks (CNN), popular deep network architectures, offer high classification accuracies for 2D images and 1D time series. This study proposes an efficient and highly accurate method for heart sound signal classification. The continuous wavelet transform method is employed to obtain scalogram images. The 2D scalogram images are fed to a deep CNN classifier. Using the heart sound dataset consisting of 4 abnormal and 1 normal heart sound subsets, this study investigates both binary classification and multi-class classification. The proposed classification method outperformed the state-of-the-art methods in the literature.
{"title":"Accurate Classification of Heart Sound Signals for Cardiovascular Disease Diagnosis by Wavelet Analysis and Convolutional Neural Network: Preliminary Results","authors":"A. Malik, Sezin Barın, M. E. Yüksel","doi":"10.1109/SIU49456.2020.9302491","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302491","url":null,"abstract":"Heart sound (HS) signals contain valuable diagnostic information for detection of heart abnormalities. The early detection of heart abnormalities plays an important role in reducing the mortality rate caused by heart diseases. Auscultation, the process of listening to heart sounds, is the first diagnostic method of heart diseases. This process is highly dependent on the physician expertise, making the diagnosis more of a subjective issue. There is ongoing research to automate heart sound diagnosis. Advances in machine learning have provided an easier, cheaper and objective diagnosis of diseases. Algorithms developed for heart sound classifications rely on several features and the accuracy of a model depends on the feature vector. The advent of deep learning (DL) provides a possible solution to overcome the overwhelming and time-consuming step of feature extraction. Convolutional neural networks (CNN), popular deep network architectures, offer high classification accuracies for 2D images and 1D time series. This study proposes an efficient and highly accurate method for heart sound signal classification. The continuous wavelet transform method is employed to obtain scalogram images. The 2D scalogram images are fed to a deep CNN classifier. Using the heart sound dataset consisting of 4 abnormal and 1 normal heart sound subsets, this study investigates both binary classification and multi-class classification. The proposed classification method outperformed the state-of-the-art methods in the literature.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122453433","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302069
M. A. Resat, M. C. Karakoç, S. Özyurt
In this work, we have combined an Alamouti spacetime block coded orthogonal frequency division multiplexing (OFDM) system with signal space diversity (SSD) in order to improve the physical layer security of a communication network under a Nakagami-m fading channel. Coordinate interleaving (CI) operation of the SSD technique is realized over the OFDM subcarriers. It is shown that various CI strategies can be used to make the correlation coefficients between the OFDM subcarrier channel gains equal to zero. The simulation results reveal that the legitimate user has a much better bit error rate performance than the eavesdropper even under the worst-case scenario where the eavesdropper somehow captures the CI strategy used between the transmitter and authorized receiver.
{"title":"Enhancement of Physical Layer Security in Alamouti OFDM Systems over Nakagami-m Fading Channels","authors":"M. A. Resat, M. C. Karakoç, S. Özyurt","doi":"10.1109/SIU49456.2020.9302069","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302069","url":null,"abstract":"In this work, we have combined an Alamouti spacetime block coded orthogonal frequency division multiplexing (OFDM) system with signal space diversity (SSD) in order to improve the physical layer security of a communication network under a Nakagami-m fading channel. Coordinate interleaving (CI) operation of the SSD technique is realized over the OFDM subcarriers. It is shown that various CI strategies can be used to make the correlation coefficients between the OFDM subcarrier channel gains equal to zero. The simulation results reveal that the legitimate user has a much better bit error rate performance than the eavesdropper even under the worst-case scenario where the eavesdropper somehow captures the CI strategy used between the transmitter and authorized receiver.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123044678","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302199
Taissir Y. Elganimi, Fatima I. Alwerfly, Akram Marseet
A multi-relay cooperative diversity protocol based on the concept of Generalized Spatial Modulation (GSM) scheme is proposed in this paper, assuming that decode-and-forward relaying protocol is adopted at relays. This scheme is referred to as Distributed Generalized Spatial Modulation (DGSM) with activating more than one relay. The system performance of the proposed diversity protocol in terms of the Symbol Error Rate (SER) is evaluated and compared to the performance of GSM and Distributed Spatial Modulation (DSM) schemes. Simulation results show that DGSM systems with activating more than one relay perform almost the same as DSM systems for the same spectral efficiency. It is also demonstrated that a performance enhancement of about 3 dB is achieved over GSM schemes for the same modulation order, which increases the energy efficiency and the reachability using the proposed model. Therefore, the proposed scheme can be effectively used in various 5G wireless networks.
{"title":"Distributed Generalized Spatial Modulation for Relay Networks","authors":"Taissir Y. Elganimi, Fatima I. Alwerfly, Akram Marseet","doi":"10.1109/SIU49456.2020.9302199","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302199","url":null,"abstract":"A multi-relay cooperative diversity protocol based on the concept of Generalized Spatial Modulation (GSM) scheme is proposed in this paper, assuming that decode-and-forward relaying protocol is adopted at relays. This scheme is referred to as Distributed Generalized Spatial Modulation (DGSM) with activating more than one relay. The system performance of the proposed diversity protocol in terms of the Symbol Error Rate (SER) is evaluated and compared to the performance of GSM and Distributed Spatial Modulation (DSM) schemes. Simulation results show that DGSM systems with activating more than one relay perform almost the same as DSM systems for the same spectral efficiency. It is also demonstrated that a performance enhancement of about 3 dB is achieved over GSM schemes for the same modulation order, which increases the energy efficiency and the reachability using the proposed model. Therefore, the proposed scheme can be effectively used in various 5G wireless networks.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123132194","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 : 2020-10-05DOI: 10.1109/SIU49456.2020.9302406
Selahattin Aktas, O. F. Gemici, I. Hokelek, H. Asmer
In this paper, a new comfort noise method is proposed for a half-duplex narrowband secure voice communication system. A late network entry (LNE) mechanism for establishing and maintaining crypto synchronization requires periodically replacing voice frames with LNE frames. The comfort noise mechanism at the receiver generates voice frames to fill the gap resulting in dropped voice frames. The proposed comfort noise approach informs the receiver about the cross correlation between the frequency responses of dropped and neighboring frames for replacing the dropped frame with the most suitable one. The simulation results show that the proposed approach increases the voice quality under various channel conditions.
{"title":"Comfort Noise Mechanism for Narrow Band Secure Voice Communication","authors":"Selahattin Aktas, O. F. Gemici, I. Hokelek, H. Asmer","doi":"10.1109/SIU49456.2020.9302406","DOIUrl":"https://doi.org/10.1109/SIU49456.2020.9302406","url":null,"abstract":"In this paper, a new comfort noise method is proposed for a half-duplex narrowband secure voice communication system. A late network entry (LNE) mechanism for establishing and maintaining crypto synchronization requires periodically replacing voice frames with LNE frames. The comfort noise mechanism at the receiver generates voice frames to fill the gap resulting in dropped voice frames. The proposed comfort noise approach informs the receiver about the cross correlation between the frequency responses of dropped and neighboring frames for replacing the dropped frame with the most suitable one. The simulation results show that the proposed approach increases the voice quality under various channel conditions.","PeriodicalId":312627,"journal":{"name":"2020 28th Signal Processing and Communications Applications Conference (SIU)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126588782","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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