Pub Date : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886037
T. Fryza, R. Mego
This paper is focused on the hardware modeling and the algorithms mapping on the digital signal processor (DSP) with the very long instruction word (VLIW) architecture, such as TMS320C6000. The general methods to develop an efficient application for the target processor combine high- and/or low-level programming languages. Although the hardware capabilities of the nowadays processors and compilers are persistently increasing, the programmers common practice is to hand-optimize critical parts of the digital signal processing algorithms in low-level assembly code. In the paper the benefit of the auxiliary tool for generating of semi-optimal codes for the DSP is presented. The functions for basic vector operations (addition, multiplication, and dot product) were proposed by this tool and the computing performances were compared to the corresponding functions from the TMS320C6000 DSP Library (DSPLIB). Comparing the functions' duration, the proposed routines achieve the average acceleration of 24 CPU cycles.
{"title":"Advanced mapping techniques for digital signal processors","authors":"T. Fryza, R. Mego","doi":"10.1109/ISSPIT.2016.7886037","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886037","url":null,"abstract":"This paper is focused on the hardware modeling and the algorithms mapping on the digital signal processor (DSP) with the very long instruction word (VLIW) architecture, such as TMS320C6000. The general methods to develop an efficient application for the target processor combine high- and/or low-level programming languages. Although the hardware capabilities of the nowadays processors and compilers are persistently increasing, the programmers common practice is to hand-optimize critical parts of the digital signal processing algorithms in low-level assembly code. In the paper the benefit of the auxiliary tool for generating of semi-optimal codes for the DSP is presented. The functions for basic vector operations (addition, multiplication, and dot product) were proposed by this tool and the computing performances were compared to the corresponding functions from the TMS320C6000 DSP Library (DSPLIB). Comparing the functions' duration, the proposed routines achieve the average acceleration of 24 CPU cycles.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115172456","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886001
Y. Alotaibi, A. Meftah, S. Selouani
This paper presents a phonetic analysis of Arabic speech language phonemes using hidden Markov model classifiers and their confusion matrices. For this purpose, a new classical Arabic speech corpus was planned and designed. The corpus is based on recitations from The Holy Quran of specific scripts. Semi-manual labeling and segmentation of the audio files along with other language resources such as a word dictionary were prepared. Recitations from The Holy Quran are highly indicative of the pronunciation of Arabic phonemes. The classifier results show that phonemes with the lowest frequencies in general have the highest error rates. Overall, the rates of correct classification are 76.04%, 93.01%, 93.59%, and 92.81% for monophone, left and right context biphone, and triphone systems, respectively.
{"title":"Classical Arabic phoneme contextual analysis using HMM classifiers","authors":"Y. Alotaibi, A. Meftah, S. Selouani","doi":"10.1109/ISSPIT.2016.7886001","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886001","url":null,"abstract":"This paper presents a phonetic analysis of Arabic speech language phonemes using hidden Markov model classifiers and their confusion matrices. For this purpose, a new classical Arabic speech corpus was planned and designed. The corpus is based on recitations from The Holy Quran of specific scripts. Semi-manual labeling and segmentation of the audio files along with other language resources such as a word dictionary were prepared. Recitations from The Holy Quran are highly indicative of the pronunciation of Arabic phonemes. The classifier results show that phonemes with the lowest frequencies in general have the highest error rates. Overall, the rates of correct classification are 76.04%, 93.01%, 93.59%, and 92.81% for monophone, left and right context biphone, and triphone systems, respectively.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130615611","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886035
H. Nakouri, M. Limam
For a set of 2D objects such as image representations, a 2DPCA approach that computes principal components of row-row and column-column covariance matrices would be more appropriate. The Generalized Low Rank Approximation of Matrices (GLRAM) approach has proved its efficiency on computation time and compression ratio over 1D principal components analysis approaches. However, GLRAM fails to efficiently account noise and outliers. To address this problem, a robust version of GLRAM, called RGLRAM is proposed. To weaken the noise effect, we propose a non-greedy iterative approach for GLRAM that maximizes data covariance in the projection subspace and minimizes the construction error. The proposed method is applied to face image recognition and shows its efficiency in handling noisy data more than GLRAM does. Experiments are performed on three benchmark face databases and results reveal that the proposed method achieves substantial results in terms of recognition accuracy, numerical stability, convergence and speed.
{"title":"Robust Generalized Low Rank Approximation of Matrices for image recognition","authors":"H. Nakouri, M. Limam","doi":"10.1109/ISSPIT.2016.7886035","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886035","url":null,"abstract":"For a set of 2D objects such as image representations, a 2DPCA approach that computes principal components of row-row and column-column covariance matrices would be more appropriate. The Generalized Low Rank Approximation of Matrices (GLRAM) approach has proved its efficiency on computation time and compression ratio over 1D principal components analysis approaches. However, GLRAM fails to efficiently account noise and outliers. To address this problem, a robust version of GLRAM, called RGLRAM is proposed. To weaken the noise effect, we propose a non-greedy iterative approach for GLRAM that maximizes data covariance in the projection subspace and minimizes the construction error. The proposed method is applied to face image recognition and shows its efficiency in handling noisy data more than GLRAM does. Experiments are performed on three benchmark face databases and results reveal that the proposed method achieves substantial results in terms of recognition accuracy, numerical stability, convergence and speed.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128558429","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886030
M. Zapf, R. Chhabra
3D-USCT-II is a novel imaging method aimed at detecting breast cancer at an early stage by using Synthetic Aperture Focusing Technique (SAFT). The excitation signal (Coded Excitation), used as an input signal, goes to the receiver transducer, and is then fed into a signal processing chain where a digital filter is used with a bandwidth from 1.66 MHz to 3.33 MHz, which is also defined as its digital bandwidth. The analog bandwidth of the signal, however, begins below 1.66 MHz. Therefore, there is considerable loss of bandwidth with the usage of this digital filter. A solution presented here makes use of modulation to assist the bandwidth increase of the digital filter. Results are then compared with metrics defined by SNR, increase in bandwidth, and increase in signal fidelity. The results show an increase in bandwidth by 15.06%, increase in SNR by 7.72% and increase in signal fidelity by 5.76%.
{"title":"Designing an optimal digital bandpass filter for 3D USCT II","authors":"M. Zapf, R. Chhabra","doi":"10.1109/ISSPIT.2016.7886030","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886030","url":null,"abstract":"3D-USCT-II is a novel imaging method aimed at detecting breast cancer at an early stage by using Synthetic Aperture Focusing Technique (SAFT). The excitation signal (Coded Excitation), used as an input signal, goes to the receiver transducer, and is then fed into a signal processing chain where a digital filter is used with a bandwidth from 1.66 MHz to 3.33 MHz, which is also defined as its digital bandwidth. The analog bandwidth of the signal, however, begins below 1.66 MHz. Therefore, there is considerable loss of bandwidth with the usage of this digital filter. A solution presented here makes use of modulation to assist the bandwidth increase of the digital filter. Results are then compared with metrics defined by SNR, increase in bandwidth, and increase in signal fidelity. The results show an increase in bandwidth by 15.06%, increase in SNR by 7.72% and increase in signal fidelity by 5.76%.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131389924","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886000
Cong Guo, Like Hui, Weiqiang Zhang, Jia Liu
Computational auditory scene analysis (CASA) system is well used in speech enhancement area in recent years. We propose a new system that combines CASA and spectral subtraction to get better enhanced speech. The CASA part consists of the latest method deep neural networks (DNNs). The original way to reconstruct the denoise signal is to use the estimated masks with direct overlap-add method ignoring the information of noise within the frames. In our system, we estimate self-adapted thresholds for each channel by Gaussian Mixture Model from the estimated ratio masks (ERMs) to separate noise and speech of each channel. In this way, we make full use of the information within frames. The results show increase in both objective and subjective evaluation.
{"title":"A speech enhancement algorithm using computational auditory scene analysis with spectral subtraction","authors":"Cong Guo, Like Hui, Weiqiang Zhang, Jia Liu","doi":"10.1109/ISSPIT.2016.7886000","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886000","url":null,"abstract":"Computational auditory scene analysis (CASA) system is well used in speech enhancement area in recent years. We propose a new system that combines CASA and spectral subtraction to get better enhanced speech. The CASA part consists of the latest method deep neural networks (DNNs). The original way to reconstruct the denoise signal is to use the estimated masks with direct overlap-add method ignoring the information of noise within the frames. In our system, we estimate self-adapted thresholds for each channel by Gaussian Mixture Model from the estimated ratio masks (ERMs) to separate noise and speech of each channel. In this way, we make full use of the information within frames. The results show increase in both objective and subjective evaluation.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128100775","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886010
Lucas D. X. Ribeiro, Jayme Milanezi, J. Costa, W. Giozza, R. K. Miranda, M. Vieira
Electricity demand time series are stochastic processes related to climate, social and economic variables. By predicting the evolution of such time series, electrical load forecasting can be performed in order to support the electrical grid planning. In this paper, we propose a Kalman based load forecasting system for daily demand forecasting. Our proposed approach incorporates a Principal Component Analysis (PCA) of the input variables obtained from linear and nonlinear transformations of the candidate time series. In order to validate our predicting scheme, data collected from Brasília distribution company has been used. Our proposed approach outperforms state-of-the-art approaches based on state space and artificial neural networks.
{"title":"PCA-Kalman based load forecasting of electric power demand","authors":"Lucas D. X. Ribeiro, Jayme Milanezi, J. Costa, W. Giozza, R. K. Miranda, M. Vieira","doi":"10.1109/ISSPIT.2016.7886010","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886010","url":null,"abstract":"Electricity demand time series are stochastic processes related to climate, social and economic variables. By predicting the evolution of such time series, electrical load forecasting can be performed in order to support the electrical grid planning. In this paper, we propose a Kalman based load forecasting system for daily demand forecasting. Our proposed approach incorporates a Principal Component Analysis (PCA) of the input variables obtained from linear and nonlinear transformations of the candidate time series. In order to validate our predicting scheme, data collected from Brasília distribution company has been used. Our proposed approach outperforms state-of-the-art approaches based on state space and artificial neural networks.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131349911","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}
The performance of Synthetic aperture radar (SAR) imagery is often significantly deteriorated by the random phase noises arose from the atmospheric turbulence or frequency jitter of the transmit signal within SAR observations. The computational time of the traditional phase retrieval based SAR autofocus algorithms is sharply increased with the size of scene. In this paper, we recast the SAR imaging problem via the phase-corrupted data as a special case of block-based quadratic compressed sensing (BBQCS) problem. We propose a novel fast SAR imaging algorithm to recover the focused well SAR image from the phase-corrupted data and reduce the computational time and memory requirement for several orders of magnitude. Experimental results show our proposed algorithm not only reduces the computational complex but also provides satisfactory reconstruction performance.
{"title":"Bl-GESPAR: A fast SAR imaging algorithm for phase noise mitigation","authors":"Qing Zhang, Xunchao Cong, Keyu Long, Yue Yang, Jiangbo Liu, Q. Wan","doi":"10.1109/ISSPIT.2016.7886043","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886043","url":null,"abstract":"The performance of Synthetic aperture radar (SAR) imagery is often significantly deteriorated by the random phase noises arose from the atmospheric turbulence or frequency jitter of the transmit signal within SAR observations. The computational time of the traditional phase retrieval based SAR autofocus algorithms is sharply increased with the size of scene. In this paper, we recast the SAR imaging problem via the phase-corrupted data as a special case of block-based quadratic compressed sensing (BBQCS) problem. We propose a novel fast SAR imaging algorithm to recover the focused well SAR image from the phase-corrupted data and reduce the computational time and memory requirement for several orders of magnitude. Experimental results show our proposed algorithm not only reduces the computational complex but also provides satisfactory reconstruction performance.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115969548","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886051
Jongmin Lee, M. Stanley, A. Spanias, C. Tepedelenlioğlu
Interpreting sensor data in Internet-of-Things applications is a challenging problem particularly in embedded systems. We consider sensor data analytics where machine learning algorithms can be fully implemented on an embedded processor/sensor board. We develop an efficient real-time realization of a Gaussian mixture model (GMM) for execution on the NXP FRDM-K64F embedded sensor board. We demonstrate the design of a customized program and data structure that generates real-time sensor features, and we show details and training/classification results for select IoT applications. The integrated hardware/software system enables real-time data analytics and continuous training and re-training of the machine learning (ML) algorithm. The real-time ML platform can accommodate several applications with lower sensor data traffic.
{"title":"Integrating machine learning in embedded sensor systems for Internet-of-Things applications","authors":"Jongmin Lee, M. Stanley, A. Spanias, C. Tepedelenlioğlu","doi":"10.1109/ISSPIT.2016.7886051","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886051","url":null,"abstract":"Interpreting sensor data in Internet-of-Things applications is a challenging problem particularly in embedded systems. We consider sensor data analytics where machine learning algorithms can be fully implemented on an embedded processor/sensor board. We develop an efficient real-time realization of a Gaussian mixture model (GMM) for execution on the NXP FRDM-K64F embedded sensor board. We demonstrate the design of a customized program and data structure that generates real-time sensor features, and we show details and training/classification results for select IoT applications. The integrated hardware/software system enables real-time data analytics and continuous training and re-training of the machine learning (ML) algorithm. The real-time ML platform can accommodate several applications with lower sensor data traffic.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121274723","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886054
H. Kusetogullari, Håkan Grahn, Niklas Lavesson
In this paper, a new approach is proposed to enhance the handwriting image by using learning-based windowing contrast enhancement and Gaussian Mixture Model (GMM). A fixed size window moves over the handwriting image and two quantitative methods which are discrete entropy (DE) and edge-based contrast measure (EBCM) are used to estimate the quality of each patch. The obtained results are used in the unsupervised learning method by using k-means clustering to assign the quality of handwriting as bad (if it is low contrast) or good (if it is high contrast). After that, if the corresponding patch is estimated as low contrast, a contrast enhancement method is applied to the window to enhance the handwriting. GMM is used as a final step to smoothly exchange information between original and enhanced images to discard the artifacts to represent the final image. The proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013. Results illustrate that proposed method performs well to enhance the handwriting comparing to the existing contrast enhancement methods.
{"title":"Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering","authors":"H. Kusetogullari, Håkan Grahn, Niklas Lavesson","doi":"10.1109/ISSPIT.2016.7886054","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886054","url":null,"abstract":"In this paper, a new approach is proposed to enhance the handwriting image by using learning-based windowing contrast enhancement and Gaussian Mixture Model (GMM). A fixed size window moves over the handwriting image and two quantitative methods which are discrete entropy (DE) and edge-based contrast measure (EBCM) are used to estimate the quality of each patch. The obtained results are used in the unsupervised learning method by using k-means clustering to assign the quality of handwriting as bad (if it is low contrast) or good (if it is high contrast). After that, if the corresponding patch is estimated as low contrast, a contrast enhancement method is applied to the window to enhance the handwriting. GMM is used as a final step to smoothly exchange information between original and enhanced images to discard the artifacts to represent the final image. The proposed method has been compared with the other contrast enhancement methods for different datasets which are Swedish historical documents, DIBCO2010, DIBCO2012 and DIBCO2013. Results illustrate that proposed method performs well to enhance the handwriting comparing to the existing contrast enhancement methods.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128963871","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 : 2016-12-01DOI: 10.1109/ISSPIT.2016.7886033
Kevin Struwe
When it comes to time- and pitch-scale modification algorithms, Phase Vocoder based approaches are widely used. However, a problem with the standard approach is the limited scaling range from 50% to 200%. For an adaptive pitch transposition method for cochlear implants, larger scaling range values that are varying on a by-frame-basis are needed. This paper shows a solution for that problem with adjusted modification and synthesis stages. The developed algorithm has a constant analysis hop size and a varying synthesis hop size. Effectively, interpolated intermediate frames are introduced to compensate for missing signal information. These frames are spaced equidistant with the reciprocal scaling factor and have linear interpolated amplitudes. As a result, the desired characteristics could be achieved by a high quality implementation of the new method. The sound quality could be evaluated at an informal listening test.
{"title":"A time scale modification with large and varying scaling factors","authors":"Kevin Struwe","doi":"10.1109/ISSPIT.2016.7886033","DOIUrl":"https://doi.org/10.1109/ISSPIT.2016.7886033","url":null,"abstract":"When it comes to time- and pitch-scale modification algorithms, Phase Vocoder based approaches are widely used. However, a problem with the standard approach is the limited scaling range from 50% to 200%. For an adaptive pitch transposition method for cochlear implants, larger scaling range values that are varying on a by-frame-basis are needed. This paper shows a solution for that problem with adjusted modification and synthesis stages. The developed algorithm has a constant analysis hop size and a varying synthesis hop size. Effectively, interpolated intermediate frames are introduced to compensate for missing signal information. These frames are spaced equidistant with the reciprocal scaling factor and have linear interpolated amplitudes. As a result, the desired characteristics could be achieved by a high quality implementation of the new method. The sound quality could be evaluated at an informal listening test.","PeriodicalId":371691,"journal":{"name":"2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115025568","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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