Pub Date : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287592
Payam Shahsavari Baboukani, C. Graversen, Jan Østergaard
It is well-known that estimation of the directed dependency between high-dimensional data sequences suffers from the "curse of dimensionality" problem. To reduce the dimensionality of the data, and thereby improve the accuracy of the estimation, we propose a new progressive input variable selection technique. Specifically, in each iteration, the remaining input variables are ranked according to a weighted sum of the amount of new information provided by the variable and the variable’s prediction accuracy. Then, the highest ranked variable is included, if it is significant enough to improve the accuracy of the prediction. A simulation study on synthetic non-linear autoregressive and Henon maps data, shows a significant improvement over existing estimator, especially in the case of small amounts of high-dimensional and highly correlated data.
{"title":"Estimation of Directed Dependencies in Time Series Using Conditional Mutual Information and Non-linear Prediction","authors":"Payam Shahsavari Baboukani, C. Graversen, Jan Østergaard","doi":"10.23919/Eusipco47968.2020.9287592","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287592","url":null,"abstract":"It is well-known that estimation of the directed dependency between high-dimensional data sequences suffers from the \"curse of dimensionality\" problem. To reduce the dimensionality of the data, and thereby improve the accuracy of the estimation, we propose a new progressive input variable selection technique. Specifically, in each iteration, the remaining input variables are ranked according to a weighted sum of the amount of new information provided by the variable and the variable’s prediction accuracy. Then, the highest ranked variable is included, if it is significant enough to improve the accuracy of the prediction. A simulation study on synthetic non-linear autoregressive and Henon maps data, shows a significant improvement over existing estimator, especially in the case of small amounts of high-dimensional and highly correlated data.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"5 1","pages":"2388-2392"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88327027","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287531
A. Mian, Elias Raninen, E. Ollila
In recent years, the use of Riemannian geometry has reportedly shown an increased performance for machine learning problems whose features lie in the symmetric positive definite (SPD) manifold. The present paper aims at reviewing several approaches based on this paradigm and provide a reproducible comparison of their output on a classic learning task of pedestrian detection. Notably, the robustness of these approaches to corrupted data will be assessed.
{"title":"A Comparative Study of Supervised Learning Algorithms for Symmetric Positive Definite Features","authors":"A. Mian, Elias Raninen, E. Ollila","doi":"10.23919/Eusipco47968.2020.9287531","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287531","url":null,"abstract":"In recent years, the use of Riemannian geometry has reportedly shown an increased performance for machine learning problems whose features lie in the symmetric positive definite (SPD) manifold. The present paper aims at reviewing several approaches based on this paradigm and provide a reproducible comparison of their output on a classic learning task of pedestrian detection. Notably, the robustness of these approaches to corrupted data will be assessed.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"3 1","pages":"950-954"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86648468","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287504
E. Dumic, F. Battisti, M. Carli, L. Cruz
The availability of 3D range scanners and RGB-D cameras is pushing the spreading of point cloud-based applications. One of the main issues of this technology, in applications where the end user is a human observer, is the presentation of the data. Three-dimensional visual information represented as point clouds can be displayed in several ways, e.g. as sets of points with varying point size or as a surface rendered using one of several available methods, such as Poisson surface interpolation. Furthermore, to increase the feeling of presence, or immersiveness, novel hardware can be used such as 3D displays and head mounted devices. However, even if 3D-able visualization devices are available, common users are more accustomed to observing visual information displayed on a 2D screen and it is not clear which combination of presentation method and device are preferred by the users. In this contribution we assess the user preference of visualization of point clouds in terms of different rendering devices and methods. A set of subjective experiments is performed, involving point clouds presented as points or rendered surfaces displayed in 2D and 3D displays. The results obtained were analysed to measure user preferences.
{"title":"Point Cloud Visualization Methods: a Study on Subjective Preferences","authors":"E. Dumic, F. Battisti, M. Carli, L. Cruz","doi":"10.23919/Eusipco47968.2020.9287504","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287504","url":null,"abstract":"The availability of 3D range scanners and RGB-D cameras is pushing the spreading of point cloud-based applications. One of the main issues of this technology, in applications where the end user is a human observer, is the presentation of the data. Three-dimensional visual information represented as point clouds can be displayed in several ways, e.g. as sets of points with varying point size or as a surface rendered using one of several available methods, such as Poisson surface interpolation. Furthermore, to increase the feeling of presence, or immersiveness, novel hardware can be used such as 3D displays and head mounted devices. However, even if 3D-able visualization devices are available, common users are more accustomed to observing visual information displayed on a 2D screen and it is not clear which combination of presentation method and device are preferred by the users. In this contribution we assess the user preference of visualization of point clouds in terms of different rendering devices and methods. A set of subjective experiments is performed, involving point clouds presented as points or rendered surfaces displayed in 2D and 3D displays. The results obtained were analysed to measure user preferences.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"78 1","pages":"595-599"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87201366","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287805
T. Huuhtanen, A. Lankinen, Alexander Jung
An emerging class of applications uses sensing surfaces, where sensor data is collected from a 2-dimensional surface covering a large spatial area. Sensing surface applications range from observing human activity to detecting failures of construction materials. Electrical impedance tomography (EIT) is an imaging technology, which has been successfully applied to imaging in several important application domains such as medicine, geophysics, and process industry. EIT is a low-cost technology offering high temporal resolution, which makes it a potential technology sensing surfaces. In this paper, we evaluate the applicability of EIT algorithms for tracking a small moving object on a 2D sensing surface. We compare standard EIT algorithms for this purpose and develop a method which models the movement of a small target on a sensing surface using hidden Markov models (HMM). Existing EIT methods are geared towards high image quality instead of smooth target trajectories, which makes them suboptimal for target tracking. Numerical experiments indicate that our proposed method outperforms existing EIT methods in target tracking accuracy.
{"title":"Target Tracking on Sensing Surface with Electrical Impedance Tomography","authors":"T. Huuhtanen, A. Lankinen, Alexander Jung","doi":"10.23919/Eusipco47968.2020.9287805","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287805","url":null,"abstract":"An emerging class of applications uses sensing surfaces, where sensor data is collected from a 2-dimensional surface covering a large spatial area. Sensing surface applications range from observing human activity to detecting failures of construction materials. Electrical impedance tomography (EIT) is an imaging technology, which has been successfully applied to imaging in several important application domains such as medicine, geophysics, and process industry. EIT is a low-cost technology offering high temporal resolution, which makes it a potential technology sensing surfaces. In this paper, we evaluate the applicability of EIT algorithms for tracking a small moving object on a 2D sensing surface. We compare standard EIT algorithms for this purpose and develop a method which models the movement of a small target on a sensing surface using hidden Markov models (HMM). Existing EIT methods are geared towards high image quality instead of smooth target trajectories, which makes them suboptimal for target tracking. Numerical experiments indicate that our proposed method outperforms existing EIT methods in target tracking accuracy.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"30 1","pages":"1817-1821"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87344806","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287492
Huanyu Zuo, P. Samarasinghe, T. Abhayapala
Intensity based soundfield reproduction methods are shown to provide impressive human perception of sound localization. However, most of the previous works in this domain either focus on a single sweet spot for the listener, or are constrained to a regular loudspeaker geometry, which is difficult to implement in real-world applications. This paper addresses both of the above challenges. We propose an intensity matching technique to optimally reproduce sound intensity at multiple sweet spots using an irregular loudspeaker array. The performance of the proposed method is evaluated by comparing it with the pressure and velocity matching method through numerical simulations and perceptual experiments. The results show that the proposed method has an improved performance.
{"title":"Intensity Based Soundfield Reproduction over Multiple Sweet Spots Using an Irregular Loudspeaker Array","authors":"Huanyu Zuo, P. Samarasinghe, T. Abhayapala","doi":"10.23919/Eusipco47968.2020.9287492","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287492","url":null,"abstract":"Intensity based soundfield reproduction methods are shown to provide impressive human perception of sound localization. However, most of the previous works in this domain either focus on a single sweet spot for the listener, or are constrained to a regular loudspeaker geometry, which is difficult to implement in real-world applications. This paper addresses both of the above challenges. We propose an intensity matching technique to optimally reproduce sound intensity at multiple sweet spots using an irregular loudspeaker array. The performance of the proposed method is evaluated by comparing it with the pressure and velocity matching method through numerical simulations and perceptual experiments. The results show that the proposed method has an improved performance.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"93 1","pages":"486-490"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80025817","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287771
Robbe Van Rompaey, M. Moonen
This paper presents a distributed adaptive algorithm for node-specific sound zoning in a wireless acoustic sensor and actuator network (WASAN), based on a network-wide acoustic contrast control (ACC) method. The goal of the ACC method is to simultaneously create node-specific zones with high signal power (bright zones) while minimizing power leakage in other node-specific zones (dark zones). To obtain this, a network-wide objective involving the acoustic coupling between all the loudspeakers and microphones in the WASAN is proposed where the optimal solution is based on a centralized generalized eigenvalue decomposition (GEVD). To allow for distributed processing, a gradient based GEVD algorithm is first proposed that minimizes the same objective. This algorithm can then be modified to allow for a fully distributed implementation, involving in-network summations and simple local processing. The algorithm is referred to as the distributed adaptive gradient based ACC algorithm (DAGACC). The proposed algorithm outperforms the non-cooperative distributed solution after only a few iterations and converges to the centralized solution, as illustrated by computer simulations.
{"title":"Distributed Adaptive Acoustic Contrast Control for Node-specific Sound Zoning in a Wireless Acoustic Sensor and Actuator Network","authors":"Robbe Van Rompaey, M. Moonen","doi":"10.23919/Eusipco47968.2020.9287771","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287771","url":null,"abstract":"This paper presents a distributed adaptive algorithm for node-specific sound zoning in a wireless acoustic sensor and actuator network (WASAN), based on a network-wide acoustic contrast control (ACC) method. The goal of the ACC method is to simultaneously create node-specific zones with high signal power (bright zones) while minimizing power leakage in other node-specific zones (dark zones). To obtain this, a network-wide objective involving the acoustic coupling between all the loudspeakers and microphones in the WASAN is proposed where the optimal solution is based on a centralized generalized eigenvalue decomposition (GEVD). To allow for distributed processing, a gradient based GEVD algorithm is first proposed that minimizes the same objective. This algorithm can then be modified to allow for a fully distributed implementation, involving in-network summations and simple local processing. The algorithm is referred to as the distributed adaptive gradient based ACC algorithm (DAGACC). The proposed algorithm outperforms the non-cooperative distributed solution after only a few iterations and converges to the centralized solution, as illustrated by computer simulations.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"30 4 1","pages":"481-485"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81588316","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287615
G. D. Sad, J. Gómez
In the present paper, a novel method for Audio-Visual Speech Recognition is introduced, aiming to minimize the intra-class errors. Based on a novel training procedure, the Complementary Models are introduced. These models aim to detect the absence of a class, in contrast to traditional models that aim to detect the presence of a class. In the proposed method, traditional models are employed in the first stage of a cascade scheme, and then the proposed complementary models are used to make the final decision on the recognition results. Experimental results in all the scenarios evaluated (different inputs modalities, three databases, four classifiers, and acoustic noisy conditions), show that a good performance is achieved with the proposed scheme. Also, better results than other reported methods in the literature over two public databases are achieved.
{"title":"Audio-Visual Speech Classification based on Absent Class Detection","authors":"G. D. Sad, J. Gómez","doi":"10.23919/Eusipco47968.2020.9287615","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287615","url":null,"abstract":"In the present paper, a novel method for Audio-Visual Speech Recognition is introduced, aiming to minimize the intra-class errors. Based on a novel training procedure, the Complementary Models are introduced. These models aim to detect the absence of a class, in contrast to traditional models that aim to detect the presence of a class. In the proposed method, traditional models are employed in the first stage of a cascade scheme, and then the proposed complementary models are used to make the final decision on the recognition results. Experimental results in all the scenarios evaluated (different inputs modalities, three databases, four classifiers, and acoustic noisy conditions), show that a good performance is achieved with the proposed scheme. Also, better results than other reported methods in the literature over two public databases are achieved.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"77 1","pages":"336-340"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80987401","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287617
Zois Boukouvalas, Monica Puerto, D. Elton, Peter W. Chung, M. Fuge
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery. A main ingredient required for machine learning is a training dataset consisting of molecular features—for example fingerprint bits, chemical descriptors, etc. that adequately characterize the corresponding molecules. However, choosing features for any application is highly non-trivial, since no "universal" method for feature selection exists. In this work, we propose a data fusion framework that uses Independent Vector Analysis to uncover underlying complementary information contained in different molecular featurization methods. Our approach takes an arbitrary number of individual feature vectors and generates a low dimensional set of features—molecular signatures—that can be used for the prediction of molecular properties and for knowledge discovery. We demonstrate this on a small and diverse dataset consisting of energetic compounds for the prediction of several energetic properties as well as for demonstrating how to provide insights onto the relationships between molecular structures and properties.
{"title":"Independent Vector Analysis for Molecular Data Fusion: Application to Property Prediction and Knowledge Discovery of Energetic Materials","authors":"Zois Boukouvalas, Monica Puerto, D. Elton, Peter W. Chung, M. Fuge","doi":"10.23919/Eusipco47968.2020.9287617","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287617","url":null,"abstract":"Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery. A main ingredient required for machine learning is a training dataset consisting of molecular features—for example fingerprint bits, chemical descriptors, etc. that adequately characterize the corresponding molecules. However, choosing features for any application is highly non-trivial, since no \"universal\" method for feature selection exists. In this work, we propose a data fusion framework that uses Independent Vector Analysis to uncover underlying complementary information contained in different molecular featurization methods. Our approach takes an arbitrary number of individual feature vectors and generates a low dimensional set of features—molecular signatures—that can be used for the prediction of molecular properties and for knowledge discovery. We demonstrate this on a small and diverse dataset consisting of energetic compounds for the prediction of several energetic properties as well as for demonstrating how to provide insights onto the relationships between molecular structures and properties.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"20 1","pages":"1030-1034"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81101048","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287806
M. Hilton, Roxana Alexandru, P. Dragotti
We investigate the problem of reconstructing signals with finite rate of innovation from non-uniform samples obtained using an integrate-and-fire system. We assume that the signal is first filtered using the derivative of a hyperbolic secant as a sampling kernel. Timing information is then obtained using an integrator and a threshold detector. The reconstruction method we propose achieves perfect reconstruction of streams of K Diracs at arbitrary time locations, or equivalently piecewise constant signals with discontinuities at arbitrary time locations, using as few as 3K+1 non-uniform samples.
{"title":"Time Encoding Using the Hyperbolic Secant Kernel","authors":"M. Hilton, Roxana Alexandru, P. Dragotti","doi":"10.23919/Eusipco47968.2020.9287806","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287806","url":null,"abstract":"We investigate the problem of reconstructing signals with finite rate of innovation from non-uniform samples obtained using an integrate-and-fire system. We assume that the signal is first filtered using the derivative of a hyperbolic secant as a sampling kernel. Timing information is then obtained using an integrator and a threshold detector. The reconstruction method we propose achieves perfect reconstruction of streams of K Diracs at arbitrary time locations, or equivalently piecewise constant signals with discontinuities at arbitrary time locations, using as few as 3K+1 non-uniform samples.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"98 5 1","pages":"2304-2308"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83603598","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 : 2021-01-24DOI: 10.23919/Eusipco47968.2020.9287832
Sisi Shi, Andrew Busch, K. Paliwal, T. Fickenscher
This paper investigates the use of short-time Discrete Cosine Transform (DCT) for speech enhancement. We denote the absolute values and signs of the DCT spectral coefficients as the Absolute Spectrum (AS) and Polarity Spectrum (PoS), respectively. We theoretically show that the noisy PoS is the best estimate of the original, under the constrained MMSE criterion. To verify this experimentally, the effect of using the noisy PoS for signal resynthesis is analysed through objective and subjective measures. The results show that when the Instantaneous SNR (ISNR) is above 0 dB, deemed as perfect, recovery of the original speech signal can be obtained only by modifying the DCT absolute spectrum. However, an accurate DFT Phase Spectrum (PhS) estimation might be required to achieve the same improvement in perceived speech quality. When the perceived quality is measured against the Segmental SNR (SSNR), it shows the PoS is more capable to conserve the speech quality than the PhS for the same level of global distortion. The results show that the noisy PoS can be used as an estimate of the clean PoS without perceivable degradation in speech quality, only if the ISNR of the noisy speech signal is above 0 dB or the SSNR is above 10.5 dB.
{"title":"On The Use of Discrete Cosine Transform Polarity Spectrum in Speech Enhancement","authors":"Sisi Shi, Andrew Busch, K. Paliwal, T. Fickenscher","doi":"10.23919/Eusipco47968.2020.9287832","DOIUrl":"https://doi.org/10.23919/Eusipco47968.2020.9287832","url":null,"abstract":"This paper investigates the use of short-time Discrete Cosine Transform (DCT) for speech enhancement. We denote the absolute values and signs of the DCT spectral coefficients as the Absolute Spectrum (AS) and Polarity Spectrum (PoS), respectively. We theoretically show that the noisy PoS is the best estimate of the original, under the constrained MMSE criterion. To verify this experimentally, the effect of using the noisy PoS for signal resynthesis is analysed through objective and subjective measures. The results show that when the Instantaneous SNR (ISNR) is above 0 dB, deemed as perfect, recovery of the original speech signal can be obtained only by modifying the DCT absolute spectrum. However, an accurate DFT Phase Spectrum (PhS) estimation might be required to achieve the same improvement in perceived speech quality. When the perceived quality is measured against the Segmental SNR (SSNR), it shows the PoS is more capable to conserve the speech quality than the PhS for the same level of global distortion. The results show that the noisy PoS can be used as an estimate of the clean PoS without perceivable degradation in speech quality, only if the ISNR of the noisy speech signal is above 0 dB or the SSNR is above 10.5 dB.","PeriodicalId":6705,"journal":{"name":"2020 28th European Signal Processing Conference (EUSIPCO)","volume":"122 1","pages":"421-425"},"PeriodicalIF":0.0,"publicationDate":"2021-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89873421","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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