Pub Date : 2013-10-23DOI: 10.1109/WASPAA.2013.6701826
S. Voran
We present an objective estimator of speech intelligibility that follows the paradigm of the Modified Rhyme Test (MRT). For each input, the estimator uses temporal correlations within articulation index bands to select one of six possible words from a list. The rate of successful word identification becomes the measure of speech intelligibility, as in the MRT. The estimator is called Articulation Band Correlation MRT (ABC-MRT). It consumes a tiny fraction of the resources required by MRT testing. ABC-MRT has been tested on a wide range of impaired speech recordings unseen during development. The resulting Pearson correlations between ABC-MRT and MRT results range from .95 to .99. These values exceed those of the other estimators tested.
我们提出了一种客观的语音可理解性评估方法，它遵循了修饰韵测试(MRT)的范式。对于每个输入，估计器使用发音索引带内的时间相关性从列表中选择六个可能的单词之一。单词识别的成功率成为衡量语音可理解性的标准，就像在MRT中一样。该估计器称为Articulation Band Correlation MRT (ABC-MRT)。它只消耗MRT测试所需资源的一小部分。ABC-MRT已在开发过程中未见过的各种受损语音记录上进行了测试。ABC-MRT和MRT结果之间的Pearson相关性在0.95到0.99之间。这些值超过了测试过的其他估计值。
{"title":"Using articulation index band correlations to objectively estimate speech intelligibility consistent with the modified rhyme test","authors":"S. Voran","doi":"10.1109/WASPAA.2013.6701826","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701826","url":null,"abstract":"We present an objective estimator of speech intelligibility that follows the paradigm of the Modified Rhyme Test (MRT). For each input, the estimator uses temporal correlations within articulation index bands to select one of six possible words from a list. The rate of successful word identification becomes the measure of speech intelligibility, as in the MRT. The estimator is called Articulation Band Correlation MRT (ABC-MRT). It consumes a tiny fraction of the resources required by MRT testing. ABC-MRT has been tested on a wide range of impaired speech recordings unseen during development. The resulting Pearson correlations between ABC-MRT and MRT results range from .95 to .99. These values exceed those of the other estimators tested.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126353000","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 : 2013-10-20DOI: 10.1109/WASPAA.2013.6701854
Alastair H. Moore, M. Brookes, P. Naylor
A roomprint is a quantifiable description of an acoustic environment which can be measured under controlled conditions and estimated from a monophonic recording made in that space. We here identify the properties required of a roomprint in forensic audio applications and review the observable characteristics of a room that, when extracted from recordings, could form the basis of a room-print. Frequency-dependent reverberation time is investigated as a promising characteristic and used in a room identification experiment giving correct identification in 96% of trials.
{"title":"Roomprints for forensic audio applications","authors":"Alastair H. Moore, M. Brookes, P. Naylor","doi":"10.1109/WASPAA.2013.6701854","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701854","url":null,"abstract":"A roomprint is a quantifiable description of an acoustic environment which can be measured under controlled conditions and estimated from a monophonic recording made in that space. We here identify the properties required of a roomprint in forensic audio applications and review the observable characteristics of a room that, when extracted from recordings, could form the basis of a room-print. Frequency-dependent reverberation time is investigated as a promising characteristic and used in a room identification experiment giving correct identification in 96% of trials.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116625099","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 : 2013-10-20DOI: 10.1109/WASPAA.2013.6701896
Xavier Alameda-Pineda, R. Horaud, B. Mourrain
This paper addresses the task of sound-source localization from time delay estimates using arbitrarily shaped non-coplanar microphone arrays. We fully exploit the direct path propagation model and our contribution is threefold: we provide a necessary and sufficient condition for a set of time delays to correspond to a sound source position, a proof of the uniqueness of this position, and a localization mapping to retrieve it. The time delay estimation task is casted into a non-linear multivariate optimization problem constrained by necessary and sufficient conditions on time delays. Two global optimization techniques to estimate time delays and localize the sound source are investigated. We report an extensive set of experiments and comparisons with state-of-the-art methods on simulated and real data in the presence of noise and reverberations.
{"title":"The geometry of sound-source localization using non-coplanar microphone arrays","authors":"Xavier Alameda-Pineda, R. Horaud, B. Mourrain","doi":"10.1109/WASPAA.2013.6701896","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701896","url":null,"abstract":"This paper addresses the task of sound-source localization from time delay estimates using arbitrarily shaped non-coplanar microphone arrays. We fully exploit the direct path propagation model and our contribution is threefold: we provide a necessary and sufficient condition for a set of time delays to correspond to a sound source position, a proof of the uniqueness of this position, and a localization mapping to retrieve it. The time delay estimation task is casted into a non-linear multivariate optimization problem constrained by necessary and sufficient conditions on time delays. Two global optimization techniques to estimate time delays and localize the sound source are investigated. We report an extensive set of experiments and comparisons with state-of-the-art methods on simulated and real data in the presence of noise and reverberations.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122082798","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701869
K. Kowalczyk, O. Thiergart, A. Craciun, Emanuël Habets
In hands-free communication applications, the main goal is to capture desired sounds, while reducing noise and interfering sounds. However, for natural-sounding telepresence systems, the spatial sound image should also be preserved. Using a recently proposed method for generating the signal of a virtual microphone (VM), one can recreate the sound image from an arbitrary point of view in the sound scene (e.g., close to a desired speaker), while being able to place the physical microphones outside the sound scene. In this paper, we present a method for synthesizing a VM signal in noisy and reverberant environments, where the estimation of the required direct and diffuse sound components is performed using two multichannel linear filters. The direct sound component is estimated using a multichannel Wiener filter, while the diffuse sound component is estimated using a linearly constrained minimum variance filter followed by a single-channel Wiener filter. Simulations in a noisy and reverberant environment show the applicability of the proposed method for sound acquisition in a scenario in which two microphone arrays are installed in a large TV.
{"title":"Sound acquisition in noisy and reverberant environments using virtual microphones","authors":"K. Kowalczyk, O. Thiergart, A. Craciun, Emanuël Habets","doi":"10.1109/WASPAA.2013.6701869","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701869","url":null,"abstract":"In hands-free communication applications, the main goal is to capture desired sounds, while reducing noise and interfering sounds. However, for natural-sounding telepresence systems, the spatial sound image should also be preserved. Using a recently proposed method for generating the signal of a virtual microphone (VM), one can recreate the sound image from an arbitrary point of view in the sound scene (e.g., close to a desired speaker), while being able to place the physical microphones outside the sound scene. In this paper, we present a method for synthesizing a VM signal in noisy and reverberant environments, where the estimation of the required direct and diffuse sound components is performed using two multichannel linear filters. The direct sound component is estimated using a multichannel Wiener filter, while the diffuse sound component is estimated using a linearly constrained minimum variance filter followed by a single-channel Wiener filter. Simulations in a noisy and reverberant environment show the applicability of the proposed method for sound acquisition in a scenario in which two microphone arrays are installed in a large TV.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115134600","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701889
Y. Lai, R. Balan, Heiko Claussen, J. Rosca
Consider a sensing system using a large number of N microphones placed in multiple dimensions to monitor a broadband acoustic field. Using all the microphones at once is impractical because of the amount of data generated. Instead, we choose a subset of D microphones to be active. Specifically, we wish to find the set of D microphones that minimizes the largest interference gain at multiple frequencies while monitoring a target of interest. A direct, combinatorial approach - testing all N choose D subsets of microphones - is impractical because of the problem size. Instead, we use a convex optimization technique that induces sparsity through a l1-penalty to determine which subset of microphones to use. We test the robustness of the our solution through simulated annealing and compare its performance against a classical beamformer which maximizes SNR. Since switching from a subset of D microphones to another subset of D microphones at every sample is possible, we construct a space-time-frequency sampling scheme that achieves near optimal performance.
{"title":"Broadband sensor location selection using convex optimization in very large scale arrays","authors":"Y. Lai, R. Balan, Heiko Claussen, J. Rosca","doi":"10.1109/WASPAA.2013.6701889","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701889","url":null,"abstract":"Consider a sensing system using a large number of N microphones placed in multiple dimensions to monitor a broadband acoustic field. Using all the microphones at once is impractical because of the amount of data generated. Instead, we choose a subset of D microphones to be active. Specifically, we wish to find the set of D microphones that minimizes the largest interference gain at multiple frequencies while monitoring a target of interest. A direct, combinatorial approach - testing all N choose D subsets of microphones - is impractical because of the problem size. Instead, we use a convex optimization technique that induces sparsity through a l1-penalty to determine which subset of microphones to use. We test the robustness of the our solution through simulated annealing and compare its performance against a classical beamformer which maximizes SNR. Since switching from a subset of D microphones to another subset of D microphones at every sample is possible, we construct a space-time-frequency sampling scheme that achieves near optimal performance.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122876159","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701851
Nathanael Perraudin, P. Balázs, P. Søndergaard
In this paper, we present a new algorithm to estimate a signal from its short-time Fourier transform modulus (STFTM). This algorithm is computationally simple and is obtained by an acceleration of the well-known Griffin-Lim algorithm (GLA). Before deriving the algorithm, we will give a new interpretation of the GLA and formulate the phase recovery problem in an optimization form. We then present some experimental results where the new algorithm is tested on various signals. It shows not only significant improvement in speed of convergence but it does as well recover the signals with a smaller error than the traditional GLA.
{"title":"A fast Griffin-Lim algorithm","authors":"Nathanael Perraudin, P. Balázs, P. Søndergaard","doi":"10.1109/WASPAA.2013.6701851","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701851","url":null,"abstract":"In this paper, we present a new algorithm to estimate a signal from its short-time Fourier transform modulus (STFTM). This algorithm is computationally simple and is obtained by an acceleration of the well-known Griffin-Lim algorithm (GLA). Before deriving the algorithm, we will give a new interpretation of the GLA and formulate the phase recovery problem in an optimization form. We then present some experimental results where the new algorithm is tested on various signals. It shows not only significant improvement in speed of convergence but it does as well recover the signals with a smaller error than the traditional GLA.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122950504","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701862
M. Niessen, T. V. Kasteren, A. Merentitis
The automatic recognition of sound events allows for novel applications in areas such as security, mobile and multimedia. In this work we present a hierarchical hidden Markov model for sound event detection that automatically clusters the inherent structure of the events into sub-events. We evaluate our approach on an IEEE audio challenge dataset consisting of office sound events and provide a systematic comparison of the various building blocks of our approach to demonstrate the effectiveness of incorporating certain dependencies in the model. The hierarchical hidden Markov model achieves an average frame-based F-measure recognition performance of 45.5% on a test dataset that was used to evaluate challenge submissions. We also show how the hierarchical model can be used as a meta-classifier, although in the particular application this did not lead to an increase in performance on the test dataset.
{"title":"Hierarchical modeling using automated sub-clustering for sound event recognition","authors":"M. Niessen, T. V. Kasteren, A. Merentitis","doi":"10.1109/WASPAA.2013.6701862","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701862","url":null,"abstract":"The automatic recognition of sound events allows for novel applications in areas such as security, mobile and multimedia. In this work we present a hierarchical hidden Markov model for sound event detection that automatically clusters the inherent structure of the events into sub-events. We evaluate our approach on an IEEE audio challenge dataset consisting of office sound events and provide a systematic comparison of the various building blocks of our approach to demonstrate the effectiveness of incorporating certain dependencies in the model. The hierarchical hidden Markov model achieves an average frame-based F-measure recognition performance of 45.5% on a test dataset that was used to evaluate challenge submissions. We also show how the hierarchical model can be used as a meta-classifier, although in the particular application this did not lead to an increase in performance on the test dataset.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"349 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041101","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701867
Dejan Markovic, F. Antonacci, A. Sarti, S. Tubaro
In this paper we propose a methodology for the estimation of the geometry of an environment based on a single Acoustic Impulse Response (AIR). The estimation algorithm makes use of tools for the modeling of propagation based on geometrical acoustics. A suitable cost function evaluates the distance between the simulated and measured AIRs. The room minimizing the cost function is chosen as the correct one. The cost function is strongly non linear. As a consequence, in order to reduce the complexity of the minimization problem, the algorithm needs a hypothesis about the class of geometry of the environment under analysis, such as rectangular or L-shaped rooms. We prove the effectiveness of the proposed algorithm with a number of simulations with increasing complexity.
{"title":"Estimation of room dimensions from a single impulse response","authors":"Dejan Markovic, F. Antonacci, A. Sarti, S. Tubaro","doi":"10.1109/WASPAA.2013.6701867","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701867","url":null,"abstract":"In this paper we propose a methodology for the estimation of the geometry of an environment based on a single Acoustic Impulse Response (AIR). The estimation algorithm makes use of tools for the modeling of propagation based on geometrical acoustics. A suitable cost function evaluates the distance between the simulated and measured AIRs. The room minimizing the cost function is chosen as the correct one. The cost function is strongly non linear. As a consequence, in order to reduce the complexity of the minimization problem, the algorithm needs a hypothesis about the class of geometry of the environment under analysis, such as rectangular or L-shaped rooms. We prove the effectiveness of the proposed algorithm with a number of simulations with increasing complexity.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130357923","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701885
J. Gunther, T. Moon
Whereas most approaches to linear speech prediction fail to account for the quasi-periodic glottal flow, this paper incorporates a model for the glottal flow derivative (GFD) directly into the linear prediction problem. A linear model for the prediction error is obtained by constructing a dictionary of time-shifted GFD pulses. The pulses are constructed by applying glottal inverse filtering (GIF) to recorded speech. Minimizing the difference between the linear prediction residual and a sparse combination of the pulses in the dictionary leads to joint estimation of the linear predictor as well as a sparse representation for the prediction error that reveals the instants of vocal tract excitation (epochs). The method is applied to voiced segments extracted from the CMU Arctic dataset which also includes electro-glottograms. Results show that the proposed method is effective in estimating the parameters of interest and that GIF-based pulses more accurately model GFD pulses occurring in real speech than pulses computed using the mathematical models.
{"title":"Sparse representation and epoch estimation of voiced speech","authors":"J. Gunther, T. Moon","doi":"10.1109/WASPAA.2013.6701885","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701885","url":null,"abstract":"Whereas most approaches to linear speech prediction fail to account for the quasi-periodic glottal flow, this paper incorporates a model for the glottal flow derivative (GFD) directly into the linear prediction problem. A linear model for the prediction error is obtained by constructing a dictionary of time-shifted GFD pulses. The pulses are constructed by applying glottal inverse filtering (GIF) to recorded speech. Minimizing the difference between the linear prediction residual and a sparse combination of the pulses in the dictionary leads to joint estimation of the linear predictor as well as a sparse representation for the prediction error that reveals the instants of vocal tract excitation (epochs). The method is applied to voiced segments extracted from the CMU Arctic dataset which also includes electro-glottograms. Results show that the proposed method is effective in estimating the parameters of interest and that GIF-based pulses more accurately model GFD pulses occurring in real speech than pulses computed using the mathematical models.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"168 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113984888","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 : 2013-10-01DOI: 10.1109/WASPAA.2013.6701824
R. Badeau, Mark D. Plumbley
Several probabilistic models involving latent components have been proposed for modelling time-frequency (TF) representations of audio signals (such as spectrograms), notably in the nonnegative matrix factorization (NMF) literature. Among them, the recent high resolution NMF (HR-NMF) model is able to take both phases and local correlations in each frequency band into account, and its potential has been illustrated in applications such as source separation and audio inpainting. In this paper, HR-NMF is extended to multichannel signals and to convolutive mixtures. A fast variational expectation-maximization (EM) algorithm is proposed to estimate the enhanced model. This algorithm is applied to a stereophonic piano signal, and proves capable of accurately modelling reverberation and restoring missing observations.
{"title":"Multichannel HR-NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain","authors":"R. Badeau, Mark D. Plumbley","doi":"10.1109/WASPAA.2013.6701824","DOIUrl":"https://doi.org/10.1109/WASPAA.2013.6701824","url":null,"abstract":"Several probabilistic models involving latent components have been proposed for modelling time-frequency (TF) representations of audio signals (such as spectrograms), notably in the nonnegative matrix factorization (NMF) literature. Among them, the recent high resolution NMF (HR-NMF) model is able to take both phases and local correlations in each frequency band into account, and its potential has been illustrated in applications such as source separation and audio inpainting. In this paper, HR-NMF is extended to multichannel signals and to convolutive mixtures. A fast variational expectation-maximization (EM) algorithm is proposed to estimate the enhanced model. This algorithm is applied to a stereophonic piano signal, and proves capable of accurately modelling reverberation and restoring missing observations.","PeriodicalId":341888,"journal":{"name":"2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114798562","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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