Pub Date : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408948
Yaoguang Wang, Liang He
Attention mechanism has been applied to the weakly supervised sound event detection (SED) and has achieved state-of-the-art performance, but most methods only concentrate along the time axis. In this paper, we propose the multi-scale time-frequency attention (MTFA) method to capture the intrinsic features at different scales both in time and frequency domain for audio tagging (AT) and SED. Our model is a unified network which can perform AT and SED simultaneously, it produces multi-scale attention-aware representations for SED with MTFA module, and a global pooling module maps the representations to presence probability of corresponding audio event for AT. To evaluate the proposed method, we conduct experiments on Task4 of Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, and it achieves 57.9% (F1-score) in AT task and 0.71 (error rate) in SED task on evaluation set, which is comparable to the state-of-the-art results in the challenge.
{"title":"A Joint Detection-Classification Model for Weakly Supervised Sound Event Detection Using Multi-Scale Attention Method","authors":"Yaoguang Wang, Liang He","doi":"10.1109/ISSPIT51521.2020.9408948","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408948","url":null,"abstract":"Attention mechanism has been applied to the weakly supervised sound event detection (SED) and has achieved state-of-the-art performance, but most methods only concentrate along the time axis. In this paper, we propose the multi-scale time-frequency attention (MTFA) method to capture the intrinsic features at different scales both in time and frequency domain for audio tagging (AT) and SED. Our model is a unified network which can perform AT and SED simultaneously, it produces multi-scale attention-aware representations for SED with MTFA module, and a global pooling module maps the representations to presence probability of corresponding audio event for AT. To evaluate the proposed method, we conduct experiments on Task4 of Detection and Classification of Acoustic Scenes and Events (DCASE) challenge, and it achieves 57.9% (F1-score) in AT task and 0.71 (error rate) in SED task on evaluation set, which is comparable to the state-of-the-art results in the challenge.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121875437","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408873
S. Chakraborty, M. Fowler
The scalar quantizer is often used in many applications due to its simplicity and ease with which it can be implemented. However, whenever we have some constraint in terms of bit rate or distortion, the vector quantizer is almost always a better choice. This is because for a given bit rate or for a given distortion, we can always design a vector quantizer that outperforms the optimal scalar quantizer. There are several algorithms to design a vector quantizer. But, the most popular algorithm is the Linde-Buzo-Gray algorithm which is based on the k-means clustering. For the LBG algorithm, we need to specify the number of clusters as well as the initial reconstruction vectors, which are then updated in successive iterations. Often, choosing the initial reconstruction vectors is not an easy task, especially when we deal with higher dimensions. A better option would be to naturally obtain the initial partitions from the given dataset. In the present article, we describe a hierarchical clustering based vector quantizer design. With our approach, we no longer need to choose the initial reconstruction vectors, but we naturally obtain the partitions for the given bit rate. Moreover, once we obtain the partitions, we simply place our reconstruction vectors at the centroid of the partitions and hence we avoid performing successive iterations and updating the clusters.
{"title":"Vector Quantizer with Fuzzy Equivalence Relations clustering","authors":"S. Chakraborty, M. Fowler","doi":"10.1109/ISSPIT51521.2020.9408873","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408873","url":null,"abstract":"The scalar quantizer is often used in many applications due to its simplicity and ease with which it can be implemented. However, whenever we have some constraint in terms of bit rate or distortion, the vector quantizer is almost always a better choice. This is because for a given bit rate or for a given distortion, we can always design a vector quantizer that outperforms the optimal scalar quantizer. There are several algorithms to design a vector quantizer. But, the most popular algorithm is the Linde-Buzo-Gray algorithm which is based on the k-means clustering. For the LBG algorithm, we need to specify the number of clusters as well as the initial reconstruction vectors, which are then updated in successive iterations. Often, choosing the initial reconstruction vectors is not an easy task, especially when we deal with higher dimensions. A better option would be to naturally obtain the initial partitions from the given dataset. In the present article, we describe a hierarchical clustering based vector quantizer design. With our approach, we no longer need to choose the initial reconstruction vectors, but we naturally obtain the partitions for the given bit rate. Moreover, once we obtain the partitions, we simply place our reconstruction vectors at the centroid of the partitions and hence we avoid performing successive iterations and updating the clusters.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117264445","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408699
S. Modak, L. Taha, E. Abdel-Raheem
The study of heartbeats in electrocardiogram (ECG) signals is very important to sustain good health. Any anomalies in the heart rhythm can be detected by carefully studying the ECG signal. The detection of the QRS is obstructed by external and internal sources of noise. Automatic detection of the QRS is achieved by diminishing these noises to a minimum by different types of filtering such as band-pass filtering, wavelet transform, and applying thresholds. This paper presents a new method of QRS detection using discrete wavelet transform (DWT), median filtering, and adaptive multilevel thresholding (AMT). The proposed method is tested for the MIT-BIH Arrhythmia database and shows a high sensitivity of 99.74%, positive predictivity of 99.88%, and a detection error rate of 0.38%. In addition to this, the proposed technique is quite robust and can adapt to signals with a low signal-to-noise ratio.
{"title":"Single Channel QRS Detection Using Wavelet And Median Denoising With Adaptive Multilevel Thresholding","authors":"S. Modak, L. Taha, E. Abdel-Raheem","doi":"10.1109/ISSPIT51521.2020.9408699","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408699","url":null,"abstract":"The study of heartbeats in electrocardiogram (ECG) signals is very important to sustain good health. Any anomalies in the heart rhythm can be detected by carefully studying the ECG signal. The detection of the QRS is obstructed by external and internal sources of noise. Automatic detection of the QRS is achieved by diminishing these noises to a minimum by different types of filtering such as band-pass filtering, wavelet transform, and applying thresholds. This paper presents a new method of QRS detection using discrete wavelet transform (DWT), median filtering, and adaptive multilevel thresholding (AMT). The proposed method is tested for the MIT-BIH Arrhythmia database and shows a high sensitivity of 99.74%, positive predictivity of 99.88%, and a detection error rate of 0.38%. In addition to this, the proposed technique is quite robust and can adapt to signals with a low signal-to-noise ratio.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121227094","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408755
Webert Montlouis, Yingxu Zhu
Direct Signal Interference (DSI) suppression is a necessary step in any passive bistatic radar system. The ground-based bistatic radar suffers significantly from the direct signal interference because of the short baseline distance between the transmitter and the receiver. But, many other bistatic radar geometries minimize the impact of this physical constraint. Each configuration has its degree of difficulties and requires less or more complicated suppression algorithms for a successful implementation. In some configurations, the power level difference between the target signals versus the direct path is not as large, therefore it is possible to use less complicated DSI techniques to pull the target signal from interference plus noise. One such bistatic geometry uses the satellite-based bistatic radar concept to perform surveillance in an area of interest close to the ground. This paper investigates the performance of a DVB-S signal using a class of iterative algorithms Normalized Least Mean Squares (NLMS), Wiener, Recursive Least Squares (RLS), and Fast Block Least Mean Squares (FBLMS) to suppress the direct signal using a satellite-based transmitter and a ground-based receiver to perform surveillance.
{"title":"On the performance of Low Computational Complexity DSI Suppression Techniques Using Satellite Transmitters","authors":"Webert Montlouis, Yingxu Zhu","doi":"10.1109/ISSPIT51521.2020.9408755","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408755","url":null,"abstract":"Direct Signal Interference (DSI) suppression is a necessary step in any passive bistatic radar system. The ground-based bistatic radar suffers significantly from the direct signal interference because of the short baseline distance between the transmitter and the receiver. But, many other bistatic radar geometries minimize the impact of this physical constraint. Each configuration has its degree of difficulties and requires less or more complicated suppression algorithms for a successful implementation. In some configurations, the power level difference between the target signals versus the direct path is not as large, therefore it is possible to use less complicated DSI techniques to pull the target signal from interference plus noise. One such bistatic geometry uses the satellite-based bistatic radar concept to perform surveillance in an area of interest close to the ground. This paper investigates the performance of a DVB-S signal using a class of iterative algorithms Normalized Least Mean Squares (NLMS), Wiener, Recursive Least Squares (RLS), and Fast Block Least Mean Squares (FBLMS) to suppress the direct signal using a satellite-based transmitter and a ground-based receiver to perform surveillance.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106402","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408742
Mario Fernando Jojoa Acosta, Begonya García-Zapirain Soto
This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson’s correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.
{"title":"Machine Learning Algorithms for Forecasting COVID 19 Confirmed Cases in America","authors":"Mario Fernando Jojoa Acosta, Begonya García-Zapirain Soto","doi":"10.1109/ISSPIT51521.2020.9408742","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408742","url":null,"abstract":"This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson’s correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"593 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116207530","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408923
K. Ntontin, Nikolaos D. Skentos, F. Lazarakis
Motivated by the importance of multiple-input and multiple output line-of-sight communication in next generation backhaul networks, in this work we provide an overhead and performance comparison between time- and frequency-domain channel estimation in a bursty 2x2 LOS environment with single-carrier transmission and frequency-domain equalization at the receiver. For both types of channel estimation, analytical expressions for the weights of the involved equalizers are provided in the case of minimum-mean square error equalization. Finally, simulation results are provided regarding their error rate comparison and a discussion concerning their training overhead requirements.
{"title":"Time- vs. Frequency-Domain Channel Estimation in MIMO LOS Frequency-Selective Channels","authors":"K. Ntontin, Nikolaos D. Skentos, F. Lazarakis","doi":"10.1109/ISSPIT51521.2020.9408923","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408923","url":null,"abstract":"Motivated by the importance of multiple-input and multiple output line-of-sight communication in next generation backhaul networks, in this work we provide an overhead and performance comparison between time- and frequency-domain channel estimation in a bursty 2x2 LOS environment with single-carrier transmission and frequency-domain equalization at the receiver. For both types of channel estimation, analytical expressions for the weights of the involved equalizers are provided in the case of minimum-mean square error equalization. Finally, simulation results are provided regarding their error rate comparison and a discussion concerning their training overhead requirements.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134277184","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408696
Zhuliang Lv, Yi Zhou, Hongqing Liu, Xiaofeng Shu, Nannan Zhang
Spatial audio is one of the most essential parts of immersive audio-visual experience such as virtual reality (VR), which reproduces the inherent spatiality of sound and the correspondence of audio-visual experience. Ambisonics is the dominant spatial audio solution due to its flexibility and fidelity. However, the production of Ambisonics audio is difficult for the public because of the requirements of expensive equipments or professional music production ability. In this work, an end-to-end Ambisonics generator for panorama video is proposed. To improve the perception of directional sound, we assume that sound field is composed of a primary sound source and an ambient sound without spatiality, and a Temporal Convolutional Network (TCN) based Primary Ambient Extractor (PAE) is proposed to separate the two parts of sound field. The directional sound is spatially encoded by the weights from audio-visual fusion network added by ambient part. Our network is evaluated with panorama video clips with first order Ambisonics. The results show that the proposed approach outperforms other methods in terms of objective evaluations.
{"title":"A TCN-based Primary Ambient Extraction in Generating Ambisonics Audio from Panorama Video","authors":"Zhuliang Lv, Yi Zhou, Hongqing Liu, Xiaofeng Shu, Nannan Zhang","doi":"10.1109/ISSPIT51521.2020.9408696","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408696","url":null,"abstract":"Spatial audio is one of the most essential parts of immersive audio-visual experience such as virtual reality (VR), which reproduces the inherent spatiality of sound and the correspondence of audio-visual experience. Ambisonics is the dominant spatial audio solution due to its flexibility and fidelity. However, the production of Ambisonics audio is difficult for the public because of the requirements of expensive equipments or professional music production ability. In this work, an end-to-end Ambisonics generator for panorama video is proposed. To improve the perception of directional sound, we assume that sound field is composed of a primary sound source and an ambient sound without spatiality, and a Temporal Convolutional Network (TCN) based Primary Ambient Extractor (PAE) is proposed to separate the two parts of sound field. The directional sound is spatially encoded by the weights from audio-visual fusion network added by ambient part. Our network is evaluated with panorama video clips with first order Ambisonics. The results show that the proposed approach outperforms other methods in terms of objective evaluations.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133418701","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408702
Webert Montlouis
To estimate the two-dimensional Directions of Arrival (DOA) of plane waves, a planar array is often used. The L-Shaped antenna structure provides a mechanism to estimate the 2D parameters without using a fully populated planar array. This antenna array geometry has been studied when we assume the source is stationary in the observation interval. It provides a more computationally efficient 2D DOA estimation. In this paper, we study the L-Shaped antenna array when the source is rapidly moving. In this case, not only we perform the DOA estimation but additional parameters such as angular velocities in azimuth and elevation are also estimated. In this presentation, we assume a white Gaussian background noise, and the Maximum Likelihood estimator is formulated.
{"title":"DOAV Estimation Using L-Shaped Antenna Array Configuration","authors":"Webert Montlouis","doi":"10.1109/ISSPIT51521.2020.9408702","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408702","url":null,"abstract":"To estimate the two-dimensional Directions of Arrival (DOA) of plane waves, a planar array is often used. The L-Shaped antenna structure provides a mechanism to estimate the 2D parameters without using a fully populated planar array. This antenna array geometry has been studied when we assume the source is stationary in the observation interval. It provides a more computationally efficient 2D DOA estimation. In this paper, we study the L-Shaped antenna array when the source is rapidly moving. In this case, not only we perform the DOA estimation but additional parameters such as angular velocities in azimuth and elevation are also estimated. In this presentation, we assume a white Gaussian background noise, and the Maximum Likelihood estimator is formulated.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115625468","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408971
Xinyu Tang, Yang Xu, Rilin Chen, Yi Zhou
In this paper, we propose an adaptive multichannel linear prediction (MCLP) algorithm based on QR-decomposition recursive least squares (QRRLS) approach for online speech dereverberation, in which a time-varying forgetting factor (VFF) control scheme is devised to adapt to dynamic acoustic scenarios. Being capable of avoiding the numerical instability problem inherent to RLS-based MCLP, QRRLS-based MCLP method shows more robustness while retains the same arithmetical complexity and fast convergence as the RLS-based methods. The VFF scheme based on the approximated derivatives of the filter coefficients is adopted to update the time-wise forgetting factor which can track the varying paths of reflections effectively. Experimental results show that the proposed VFF-QRRLS-based MCLP algorithm improves the performance of speech dereverberation and also enjoys a fast tracking capability and numerical robustness compared with the conventional adaptive MCLP algorithms.
{"title":"A Time-Varying Forgetting Factor-Based QRRLS Algorithm for Multichannel Speech Dereverberation","authors":"Xinyu Tang, Yang Xu, Rilin Chen, Yi Zhou","doi":"10.1109/ISSPIT51521.2020.9408971","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408971","url":null,"abstract":"In this paper, we propose an adaptive multichannel linear prediction (MCLP) algorithm based on QR-decomposition recursive least squares (QRRLS) approach for online speech dereverberation, in which a time-varying forgetting factor (VFF) control scheme is devised to adapt to dynamic acoustic scenarios. Being capable of avoiding the numerical instability problem inherent to RLS-based MCLP, QRRLS-based MCLP method shows more robustness while retains the same arithmetical complexity and fast convergence as the RLS-based methods. The VFF scheme based on the approximated derivatives of the filter coefficients is adopted to update the time-wise forgetting factor which can track the varying paths of reflections effectively. Experimental results show that the proposed VFF-QRRLS-based MCLP algorithm improves the performance of speech dereverberation and also enjoys a fast tracking capability and numerical robustness compared with the conventional adaptive MCLP algorithms.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126065900","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 : 2020-12-09DOI: 10.1109/ISSPIT51521.2020.9408685
F. Ishiyama, Y. Toriumi
Capacitors are the parts of a power supply unit that deteriorate most easily. Among types of power supply unit, AC adapters are the ones for which it is not possible to check the leakage or bulging of capacitors, because they are sealed and invisible. Therefore, we focused on the electromagnetic noise which deteriorated AC adapters emit on the power line. We measured their noise and analyzed them with our own method of mode decomposition. It was found that the intensity of the noise is proportional to the internal resistance of the deteriorated capacitors measured in the hot condition.
{"title":"Aging Estimation of an AC Adapter from Generated Electromagnetic Noise","authors":"F. Ishiyama, Y. Toriumi","doi":"10.1109/ISSPIT51521.2020.9408685","DOIUrl":"https://doi.org/10.1109/ISSPIT51521.2020.9408685","url":null,"abstract":"Capacitors are the parts of a power supply unit that deteriorate most easily. Among types of power supply unit, AC adapters are the ones for which it is not possible to check the leakage or bulging of capacitors, because they are sealed and invisible. Therefore, we focused on the electromagnetic noise which deteriorated AC adapters emit on the power line. We measured their noise and analyzed them with our own method of mode decomposition. It was found that the intensity of the noise is proportional to the internal resistance of the deteriorated capacitors measured in the hot condition.","PeriodicalId":111385,"journal":{"name":"2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126085537","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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