Pub Date : 2016-09-01DOI: 10.1109/SPA.2016.7763602
Ahmed Desouky, A. El-Mahdy
In this paper, a down-link cooperative communication scheme is proposed using full-rate Quasi-Orthogonal Space-time Block Codes (QOSTBC), employing asynchronous mobile Decode and Forward (DF) relays in a Rayleigh fading wireless environment. The channel used is a partially-coherent relay channel (PCRC), where the first hop is non-coherent and the second is coherent. Base station broadcasts using four transmitting antennas, towards all relay nodes in range over the first hop. Each intermediate relay uses Minimum Decoding Complexity (MDC) decoder achieving linear complexity. A relay selection technique is applied, electing the best performing intermediate nodes based on the channel response and path loss due to location relative to the destination node. A power allocation technique is adopted, which ranks based on the chosen relays performance. Relays transmit over four pre-defined time slots per frame. The destination node uses a Maximum-Likelihood (ML) decoder.
{"title":"Asynchronous down-link cooperative communication scheme in Rayleigh fading wireless environment","authors":"Ahmed Desouky, A. El-Mahdy","doi":"10.1109/SPA.2016.7763602","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763602","url":null,"abstract":"In this paper, a down-link cooperative communication scheme is proposed using full-rate Quasi-Orthogonal Space-time Block Codes (QOSTBC), employing asynchronous mobile Decode and Forward (DF) relays in a Rayleigh fading wireless environment. The channel used is a partially-coherent relay channel (PCRC), where the first hop is non-coherent and the second is coherent. Base station broadcasts using four transmitting antennas, towards all relay nodes in range over the first hop. Each intermediate relay uses Minimum Decoding Complexity (MDC) decoder achieving linear complexity. A relay selection technique is applied, electing the best performing intermediate nodes based on the channel response and path loss due to location relative to the destination node. A power allocation technique is adopted, which ranks based on the chosen relays performance. Relays transmit over four pre-defined time slots per frame. The destination node uses a Maximum-Likelihood (ML) decoder.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125334728","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763599
K. Nurzynska, B. Smolka
Emotions are one of the most important cues of non-verbal communication, therefore are easily recognized by people. However, correct identification of intentions causes many problems, because telling apart the true emotion expression from the fake one is difficult. Therefore, there is a wide search nowadays for automatic techniques, which use computers to analyze behavior of people and learn the differences between spontaneous and posed emotional gestures. In this research we concentrate on the analysis of facial expressions in order to recognize between Duchenne (real) and fake smiles. Our approach treats the input movie as a dynamic texture and exploits LBP-TOP technique for its description. Additionally, the average facial image is removed from consideration as it is assumed, that only tiny changes differ the smile characters. The results obtained on UVA-NEMO dataset proved that it is possible to classify accurately between the spontaneous and posed smiles. Moreover, the influence of facial parts as well as changes due to a phase of smile are investigated.
{"title":"Smile veracity recognition using 3D texture features for image sequence processing","authors":"K. Nurzynska, B. Smolka","doi":"10.1109/SPA.2016.7763599","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763599","url":null,"abstract":"Emotions are one of the most important cues of non-verbal communication, therefore are easily recognized by people. However, correct identification of intentions causes many problems, because telling apart the true emotion expression from the fake one is difficult. Therefore, there is a wide search nowadays for automatic techniques, which use computers to analyze behavior of people and learn the differences between spontaneous and posed emotional gestures. In this research we concentrate on the analysis of facial expressions in order to recognize between Duchenne (real) and fake smiles. Our approach treats the input movie as a dynamic texture and exploits LBP-TOP technique for its description. Additionally, the average facial image is removed from consideration as it is assumed, that only tiny changes differ the smile characters. The results obtained on UVA-NEMO dataset proved that it is possible to classify accurately between the spontaneous and posed smiles. Moreover, the influence of facial parts as well as changes due to a phase of smile are investigated.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"72 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122902291","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763611
Syed Mohd Zahid Syed Zainal Ariffin, N. Jamil, P. Rahman
As the research in ear biometrics grows, there is a need to have a publicly available ear images database for researchers to test and validate their methods. There are many available ear databases captured in visible light domain. However, images in those databases are mostly acquired in a controlled environment, not in naturally illuminated environments. Up to the point of writing, there is no thermalbased ear image dataset available for research usage. In this paper, we introduce our ear image datasets that were captured in both visible and thermal domain. We present the detailed process of image acquisition, post-processing, and preparation of the ear datasets. The ear dataset consists of 2,200 images captured from 55 subjects in five different illumination conditions, ranging from 2 lux to 10,700 lux, measured using lux meter.
{"title":"DIAST variability illuminated thermal and visible ear images datasets","authors":"Syed Mohd Zahid Syed Zainal Ariffin, N. Jamil, P. Rahman","doi":"10.1109/SPA.2016.7763611","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763611","url":null,"abstract":"As the research in ear biometrics grows, there is a need to have a publicly available ear images database for researchers to test and validate their methods. There are many available ear databases captured in visible light domain. However, images in those databases are mostly acquired in a controlled environment, not in naturally illuminated environments. Up to the point of writing, there is no thermalbased ear image dataset available for research usage. In this paper, we introduce our ear image datasets that were captured in both visible and thermal domain. We present the detailed process of image acquisition, post-processing, and preparation of the ear datasets. The ear dataset consists of 2,200 images captured from 55 subjects in five different illumination conditions, ranging from 2 lux to 10,700 lux, measured using lux meter.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"27 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114455056","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763576
K. R. Rao
In the family of video coding standards, HEVC has the promise and potential to replace/supplement all the existing standards (MPEG and H.26x series including H.264/AVC). While the complexity of the HEVC encoder is several times that of the H.264/AVC, the decoder complexity is within the range of the latter. Researchers are exploring about reducing the HEVC encoder complexity · Kim et al have shown that motion estimation (ME) occupies 77–81% of HEVC encoder implementation. Hence the focus has been in reducing the ME complexity. Several researchers have implemented performance comparison of HEVC with other standards such as H.264/AVC, MPEG-4 Part 2 visual, H.262/PEG-2 Video, H.263, and VP9 and also with image coding standards such as JPEG2000, JPEG-LS, and JPEG-XR. Several tests have shown that HEVC provides improved compression efficiency up to 50% bit rate reduction for the same subjective video quality compared to H.264/AVC. Besides addressing all current applications, HEVC is designed and developed to focus on two key issues: increased video resolution — up to 8k×4k — and increased use of parallel processing architecture. Brief description of the HEVC is provided. However for details and implementation, the reader is referred to the JCT-VC documents, overview papers, keynote speeches, tutorials, panel discussions, poster sessions, special issues, test models (TM/HM), web/ftp site, open source software, test sequences, anchor bit streams and the latest books on HEVC. Also researchers are exploring transcoding between HEVC and other standards such as MPEG-2 and H.264. Further extensions to HEVC are scalable video coding (SVC), 3D video/multiview video coding and range extensions which include screen content coding (SCC), bit depths larger than 10 bits and color sampling of 4:2:2 and 4:4:4. SCC in general refers to computer generated objects and screen shots from computer applications (both images and videos) and may require lossless coding. Some of these extensions have been finalized by the end of 2014 (time frame for SCC is late 2016). They also provide fertile ground for R & D. Iguchi et al have already developed a hardware encoder for super hi-vision (SHV) i.e., ultra HDTV at 7680×4320 pixel resolution. Also real-time hardware implementation of HEVC encoder for 1080p HD video has been done. NHK is planning SHV experimental broadcasting in 2016. A 249-Mpixel/s HEVC video decoder chip for 4k Ultra-HD applications has already been developed. Bross et al have shown that real time software decoding of 4K (3840×2160) video with HEVC is feasible on current desktop CPUs using four CPU cores. They also state that encoding 4K video in real time on the other hand is a challenge. Multimedia research group (MRC) predicts 2 billion HEVC based devices by end of 2016.
在视频编码标准家族中，HEVC具有替代/补充现有所有标准(MPEG和H.26x系列，包括H.264/AVC)的前景和潜力。虽然HEVC编码器的复杂度是H.264/AVC的数倍，但解码器的复杂度在后者的范围内。研究人员正在探索如何降低HEVC编码器的复杂性·Kim等人已经表明，运动估计(ME)占据了HEVC编码器实现的77-81%。因此，重点是降低ME的复杂性。一些研究人员已经将HEVC与其他标准(如H.264/AVC、MPEG-4 Part 2 visual、H.262/PEG-2 Video、H.263和VP9)以及与图像编码标准(如JPEG2000、JPEG-LS和JPEG-XR)进行了性能比较。几项测试表明，与H.264/AVC相比，HEVC在相同的主观视频质量下提供了更高的压缩效率，比特率降低了50%。除了解决当前所有应用之外，HEVC的设计和开发主要关注两个关键问题:提高视频分辨率(高达8k×4k)和增加并行处理架构的使用。简要介绍了HEVC。然而，关于细节和实现，读者可以参考JCT-VC文档、概述论文、主题演讲、教程、小组讨论、海报会议、特刊、测试模型(TM/HM)、web/ftp站点、开源软件、测试序列、锚位流和最新的HEVC书籍。此外，研究人员还在探索HEVC与其他标准(如MPEG-2和H.264)之间的转码。HEVC的进一步扩展是可扩展视频编码(SVC)， 3D视频/多视图视频编码和范围扩展，包括屏幕内容编码(SCC)，大于10位的位深和4:2:2和4:4:4的颜色采样。SCC一般是指计算机生成的对象和从计算机应用程序(图像和视频)截取的屏幕截图，可能需要无损编码。其中一些扩展已于2014年底完成(SCC的时间框架为2016年底)。它们也为研发提供了肥沃的土壤。井口等人已经开发了一种用于超高清(SHV)的硬件编码器，即7680×4320像素分辨率的超高清电视。并对1080p高清视频的HEVC编码器进行了实时硬件实现。NHK计划在2016年进行SHV实验广播。目前已经开发出一款用于4k超高清应用的249mpixel /s HEVC视频解码器芯片。Bross等人已经证明，使用HEVC对4K (3840×2160)视频进行实时软件解码在当前使用四个CPU内核的桌面CPU上是可行的。他们还表示，另一方面，对4K视频进行实时编码是一个挑战。多媒体研究集团(MRC)预测，到2016年底，基于HEVC的设备将达到20亿台。
{"title":"High efficiency video coding","authors":"K. R. Rao","doi":"10.1109/SPA.2016.7763576","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763576","url":null,"abstract":"In the family of video coding standards, HEVC has the promise and potential to replace/supplement all the existing standards (MPEG and H.26x series including H.264/AVC). While the complexity of the HEVC encoder is several times that of the H.264/AVC, the decoder complexity is within the range of the latter. Researchers are exploring about reducing the HEVC encoder complexity · Kim et al have shown that motion estimation (ME) occupies 77–81% of HEVC encoder implementation. Hence the focus has been in reducing the ME complexity. Several researchers have implemented performance comparison of HEVC with other standards such as H.264/AVC, MPEG-4 Part 2 visual, H.262/PEG-2 Video, H.263, and VP9 and also with image coding standards such as JPEG2000, JPEG-LS, and JPEG-XR. Several tests have shown that HEVC provides improved compression efficiency up to 50% bit rate reduction for the same subjective video quality compared to H.264/AVC. Besides addressing all current applications, HEVC is designed and developed to focus on two key issues: increased video resolution — up to 8k×4k — and increased use of parallel processing architecture. Brief description of the HEVC is provided. However for details and implementation, the reader is referred to the JCT-VC documents, overview papers, keynote speeches, tutorials, panel discussions, poster sessions, special issues, test models (TM/HM), web/ftp site, open source software, test sequences, anchor bit streams and the latest books on HEVC. Also researchers are exploring transcoding between HEVC and other standards such as MPEG-2 and H.264. Further extensions to HEVC are scalable video coding (SVC), 3D video/multiview video coding and range extensions which include screen content coding (SCC), bit depths larger than 10 bits and color sampling of 4:2:2 and 4:4:4. SCC in general refers to computer generated objects and screen shots from computer applications (both images and videos) and may require lossless coding. Some of these extensions have been finalized by the end of 2014 (time frame for SCC is late 2016). They also provide fertile ground for R & D. Iguchi et al have already developed a hardware encoder for super hi-vision (SHV) i.e., ultra HDTV at 7680×4320 pixel resolution. Also real-time hardware implementation of HEVC encoder for 1080p HD video has been done. NHK is planning SHV experimental broadcasting in 2016. A 249-Mpixel/s HEVC video decoder chip for 4k Ultra-HD applications has already been developed. Bross et al have shown that real time software decoding of 4K (3840×2160) video with HEVC is feasible on current desktop CPUs using four CPU cores. They also state that encoding 4K video in real time on the other hand is a challenge. Multimedia research group (MRC) predicts 2 billion HEVC based devices by end of 2016.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116676293","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763603
Sarah Imam, A. El-Mahdy
The evolving fifth generation (5G) cellular wireless networks provide higher data rates, enhanced end-user quality-of-experience (QoE), reduced end-to-end latency, and lower energy consumption. The 5G networks will consist of macrocells, femtocells and picocells with heterogeneous characteristics and capacities which will result in a multi-tier architecture. Hence, interference is one of the main problems in such systems and it will cause degradation in the system performance. Interference alignment techniques can be used to solve this problem. In this paper, a proposed cross-tier interference management algorithm for coexisting two-tier networks is introduced. First at each receiver, we calculate the sum of the square of the channel gains from each macro user to this receiver and the average of those sum values is taken as a threshold. Macro users whose sum values are greater than this threshold are selected and aligned at the femto receiver. This alignment is done by using precoders at the transmitters. In the proposed algorithm, the subspace in which the interference signals would be aligned is identified then a distributed algorithm is applied to identify the precoders needed at the selected interferers. Numerical results demonstrate the effectiveness of selective interference alignment for uplink interference management. The Cumulative Distribution Function (CDF) is used to measure the system performance.
不断发展的第五代(5G)蜂窝无线网络提供更高的数据速率、增强的最终用户体验质量(QoE)、更少的端到端延迟和更低的能耗。5G网络将由具有异构特性和容量的宏蜂窝、飞蜂窝和皮蜂窝组成，这将导致多层架构。因此，干扰是这类系统的主要问题之一，它会导致系统性能的下降。干涉对准技术可以用来解决这个问题。本文提出了一种共存两层网络的跨层干扰管理算法。首先，在每个接收器上，我们计算从每个宏用户到该接收器的信道增益的平方和，并将这些和值的平均值作为阈值。选择总和大于此阈值的宏用户并在femto接收器上对齐。这种校准是通过在发射机上使用预编码器来完成的。该算法首先确定干扰信号对齐的子空间，然后采用分布式算法识别所选干扰处所需的预编码器。数值结果表明了选择性干扰对准在上行干扰管理中的有效性。CDF (Cumulative Distribution Function)用于度量系统的性能。
{"title":"Selective interference alignment in 5G networks","authors":"Sarah Imam, A. El-Mahdy","doi":"10.1109/SPA.2016.7763603","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763603","url":null,"abstract":"The evolving fifth generation (5G) cellular wireless networks provide higher data rates, enhanced end-user quality-of-experience (QoE), reduced end-to-end latency, and lower energy consumption. The 5G networks will consist of macrocells, femtocells and picocells with heterogeneous characteristics and capacities which will result in a multi-tier architecture. Hence, interference is one of the main problems in such systems and it will cause degradation in the system performance. Interference alignment techniques can be used to solve this problem. In this paper, a proposed cross-tier interference management algorithm for coexisting two-tier networks is introduced. First at each receiver, we calculate the sum of the square of the channel gains from each macro user to this receiver and the average of those sum values is taken as a threshold. Macro users whose sum values are greater than this threshold are selected and aligned at the femto receiver. This alignment is done by using precoders at the transmitters. In the proposed algorithm, the subspace in which the interference signals would be aligned is identified then a distributed algorithm is applied to identify the precoders needed at the selected interferers. Numerical results demonstrate the effectiveness of selective interference alignment for uplink interference management. The Cumulative Distribution Function (CDF) is used to measure the system performance.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128319679","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763588
D. Bismor, Krzysztof Bartnicki
This paper describes a project aiming at development of an embedded system speech enhancement solution. It was assumed that a low-cost, low-power, fixed-point processor will be used. It was also assumed that both the stationary and nonstationary noises should be removed. To meet the assumptions, the EZDSP5535 evaluation board, with the TMS320C5535 fixed-point digital signal processor, was selected as a hardware platform. Three speech enhancement algorithms were implemented: the FIR filtering, the adaptive line enhancement, and the spectral subtraction. The last algorithm, being a frequency-domain algorithm, utilizes the FFT co-processor available in the TMS320C5535 chip. All the algorithms can work simultaneously. Examples of good final performance are shown.
{"title":"Real-time advanced speech enhancement using low-power, fixed-point hardware","authors":"D. Bismor, Krzysztof Bartnicki","doi":"10.1109/SPA.2016.7763588","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763588","url":null,"abstract":"This paper describes a project aiming at development of an embedded system speech enhancement solution. It was assumed that a low-cost, low-power, fixed-point processor will be used. It was also assumed that both the stationary and nonstationary noises should be removed. To meet the assumptions, the EZDSP5535 evaluation board, with the TMS320C5535 fixed-point digital signal processor, was selected as a hardware platform. Three speech enhancement algorithms were implemented: the FIR filtering, the adaptive line enhancement, and the spectral subtraction. The last algorithm, being a frequency-domain algorithm, utilizes the FFT co-processor available in the TMS320C5535 chip. All the algorithms can work simultaneously. Examples of good final performance are shown.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131841212","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763583
P. Szczuko
A rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification for real and imagery motion. Results are commented and further research is proposed.
{"title":"Rough set-based classification of EEG signals related to real and imagery motion","authors":"P. Szczuko","doi":"10.1109/SPA.2016.7763583","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763583","url":null,"abstract":"A rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification for real and imagery motion. Results are commented and further research is proposed.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132867874","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763577
B. Moore
The quality of an audio device, such as a microphone, amplifier, or headphone, depends on how accurately the device transmits the properties of the sound source to the ear(s) of the listener. Two types of “distortion” can occur in this transmission: (1) “Linear” distortion, which may be described as a deviation of the frequency response from the “target” response; (2) Nonlinear distortion, which is characterised by frequency components in the output of the device that were not present in the input. These two forms of distortion have different perceptual effects. Their effects on sound quality can be predicted using a model of auditory processing with the following stages: (1) A filter to take into account the transmission of sound from the device to the ear of the listener; (2) A filter to simulate the effects of transmission through the middle ear; (3) An array of bandpass filters to simulate the auditory filters that exist in the cochlea of the inner ear. For predicting the perceptual effects of linear distortion, a model operating in the frequency domain can be used. For predicting the perceptual effects of nonlinear distortion, a model operating in the time domain is required, since the detailed waveforms at the outputs of the auditory filters need to be considered. The models described have been shown to give accurate predictions for a wide range of “artificial” and “real” linear and nonlinear distortions.
{"title":"Computational models for predicting sound quality","authors":"B. Moore","doi":"10.1109/SPA.2016.7763577","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763577","url":null,"abstract":"The quality of an audio device, such as a microphone, amplifier, or headphone, depends on how accurately the device transmits the properties of the sound source to the ear(s) of the listener. Two types of “distortion” can occur in this transmission: (1) “Linear” distortion, which may be described as a deviation of the frequency response from the “target” response; (2) Nonlinear distortion, which is characterised by frequency components in the output of the device that were not present in the input. These two forms of distortion have different perceptual effects. Their effects on sound quality can be predicted using a model of auditory processing with the following stages: (1) A filter to take into account the transmission of sound from the device to the ear of the listener; (2) A filter to simulate the effects of transmission through the middle ear; (3) An array of bandpass filters to simulate the auditory filters that exist in the cochlea of the inner ear. For predicting the perceptual effects of linear distortion, a model operating in the frequency domain can be used. For predicting the perceptual effects of nonlinear distortion, a model operating in the time domain is required, since the detailed waveforms at the outputs of the auditory filters need to be considered. The models described have been shown to give accurate predictions for a wide range of “artificial” and “real” linear and nonlinear distortions.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133772672","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763630
Radoslaw Weychan, T. Marciniak, A. Dabrowski
In this paper an analysis of multithread approach to speaker recognition is presented. Two cases have been investigated — the use of multithreading first, to reduce the total time of training and testing (when processing the speaker database) and second, to reduce the time of a single speaker recognition (e.g. in voice-based access systems). We have investigated the processing time in function of a number of threads in order to find the optimal number for 8-core CPU. The obtained results show that the computation time can be reduced even over 5 times for this kind of processor. The prepared implementation is available on Github source code repository.
{"title":"Multithreaded Java approach to speaker recognition","authors":"Radoslaw Weychan, T. Marciniak, A. Dabrowski","doi":"10.1109/SPA.2016.7763630","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763630","url":null,"abstract":"In this paper an analysis of multithread approach to speaker recognition is presented. Two cases have been investigated — the use of multithreading first, to reduce the total time of training and testing (when processing the speaker database) and second, to reduce the time of a single speaker recognition (e.g. in voice-based access systems). We have investigated the processing time in function of a number of threads in order to find the optimal number for 8-core CPU. The obtained results show that the computation time can be reduced even over 5 times for this kind of processor. The prepared implementation is available on Github source code repository.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114306364","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 : 2016-09-01DOI: 10.1109/SPA.2016.7763608
Vinay Chakravarthi Gogineni, Subrahmanyam Mula, R. Das, M. Chakraborty
For real-time sparse systems identification applications, Proportionate-type Least Mean Square (Pt-LMS) algorithms are often preferred to their normalized counterparts (Pt-NLMS) due to lower computational complexity of the former algorithms. In this paper, we present the convergence analysis of Pt-LMS algorithms. Without any assumptions on input, both first and second order convergence analysis are carried out and new convergence bounds are obtained. In particular, it establishes the universality of the steady-state mean square deviation. Detailed simulation results are presented to validate the analytical results.
{"title":"Performance analysis of proportionate-type LMS algorithms","authors":"Vinay Chakravarthi Gogineni, Subrahmanyam Mula, R. Das, M. Chakraborty","doi":"10.1109/SPA.2016.7763608","DOIUrl":"https://doi.org/10.1109/SPA.2016.7763608","url":null,"abstract":"For real-time sparse systems identification applications, Proportionate-type Least Mean Square (Pt-LMS) algorithms are often preferred to their normalized counterparts (Pt-NLMS) due to lower computational complexity of the former algorithms. In this paper, we present the convergence analysis of Pt-LMS algorithms. Without any assumptions on input, both first and second order convergence analysis are carried out and new convergence bounds are obtained. In particular, it establishes the universality of the steady-state mean square deviation. Detailed simulation results are presented to validate the analytical results.","PeriodicalId":413894,"journal":{"name":"2016 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114576034","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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