Relevant bibliographies by topics / Perceptual linear predictive

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Perceptual linear predictive'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Perceptual linear predictive"

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Hermansky, Hynek. "Perceptual linear predictive (PLP) analysis of speech." Journal of the Acoustical Society of America 87, no. 4 (April 1990): 1738–52. http://dx.doi.org/10.1121/1.399423.

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Chen, Sai, Hong Cui Wang, Jia Jia, Ye Teng An, and Jian Wu Dang. "Comparison of Mel Frequency Ceptrum Coefficient and Perceptual Linear Predictive in Perceptual Measurement of Chinese Initials." Applied Mechanics and Materials 411-414 (September 2013): 291–97. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.291.

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Many works have been done in the methods of improving performance by proposing new speech characteristics and new perception measurements. However, they only focus on one of the two aspects. In this paper, we try to study the relationship between them. That is, we discuss which acoustic features or their combinations are the most consistent with the real perception of Chinese initials. We propose a method that can measure the acoustic distance and keep it monotonically related to the perceptual distance of Chinese initials. We first define the acoustic distance and perceptual distance between different Chinese initials, and single out a proper combination of acoustic features and two compatible distance metrics by conducting clustering analysis on the samples of all types of Chinese initials using MFCC and PLP. Based on the data provided by the General Hospital of the People's Liberation Army, we then calculate the acoustic distance and perceptual distance. Finally, we calculate the Spearman's rho between two types of distance corresponding to the two calculation method. The experiment results show that there is a relatively high strength of monotonic relationship with the selected acoustic features between two types of distance.
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Kowler, Eileen, Jason F. Rubinstein, Elio M. Santos, and Jie Wang. "Predictive Smooth Pursuit Eye Movements." Annual Review of Vision Science 5, no. 1 (September 15, 2019): 223–46. http://dx.doi.org/10.1146/annurev-vision-091718-014901.

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Smooth pursuit eye movements maintain the line of sight on smoothly moving targets. Although often studied as a response to sensory motion, pursuit anticipates changes in motion trajectories, thus reducing harmful consequences due to sensorimotor processing delays. Evidence for predictive pursuit includes ( a) anticipatory smooth eye movements (ASEM) in the direction of expected future target motion that can be evoked by perceptual cues or by memory for recent motion, ( b) pursuit during periods of target occlusion, and ( c) improved accuracy of pursuit with self-generated or biologically realistic target motions. Predictive pursuit has been linked to neural activity in the frontal cortex and in sensory motion areas. As behavioral and neural evidence for predictive pursuit grows and statistically based models augment or replace linear systems approaches, pursuit is being regarded less as a reaction to immediate sensory motion and more as a predictive response, with retinal motion serving as one of a number of contributing cues.
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Lange, Elke B., and Klaus Frieler. "Challenges and Opportunities of Predicting Musical Emotions with Perceptual and Automatized Features." Music Perception 36, no. 2 (December 1, 2018): 217–42. http://dx.doi.org/10.1525/mp.2018.36.2.217.

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Music information retrieval (MIR) is a fast-growing research area. One of its aims is to extract musical characteristics from audio. In this study, we assumed the roles of researchers without further technical MIR experience and set out to test in an exploratory way its opportunities and challenges in the specific context of musical emotion perception. Twenty sound engineers rated 60 musical excerpts from a broad range of styles with respect to 22 spectral, musical, and cross-modal features (perceptual features) and perceived emotional expression. In addition, we extracted 86 features (acoustic features) of the excerpts with the MIRtoolbox (Lartillot & Toiviainen, 2007). First, we evaluated the perceptual and extracted acoustic features. Both perceptual and acoustic features posed statistical challenges (e.g., perceptual features were often bimodally distributed, and acoustic features highly correlated). Second, we tested the suitability of the acoustic features for modeling perceived emotional content. Four nearly disjunctive feature sets provided similar results, implying a certain arbitrariness of feature selection. We compared the predictive power of perceptual and acoustic features using linear mixed effects models, but the results were inconclusive. We discuss critical points and make suggestions to further evaluate MIR tools for modeling music perception and processing.
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Ding, Jian Li, and Yong Yang. "Automatic Recognition of Aircraft Noise with PLP Method." Applied Mechanics and Materials 160 (March 2012): 145–49. http://dx.doi.org/10.4028/www.scientific.net/amm.160.145.

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This paper proposes a modified auditory feature extraction algorithm based on perceptual linear predictive analysis which is more suitable for automatic recognition of aircraft noise. In this algorithm, a different distribution of filter-bank is introduced in order to fit the physical characteristic of aircraft noise and the result shows that the modified method indeed performs better. The effect of Gammatone filter in improving the robustness of recognition algorithm is also demonstrated in the experiment.
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Habeck, Christian, Qolamreza Razlighi, and Yaakov Stern. "Predictive utility of task-related functional connectivity vs. voxel activation." PLOS ONE 16, no. 4 (April 8, 2021): e0249947. http://dx.doi.org/10.1371/journal.pone.0249947.

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Functional connectivity, both in resting state and task performance, has steadily increased its share of neuroimaging research effort in the last 1.5 decades. In the current study, we investigated the predictive utility regarding behavioral performance and task information for 240 participants, aged 20–77, for both voxel activation and functional connectivity in 12 cognitive tasks, belonging to 4 cognitive reference domains (Episodic Memory, Fluid Reasoning, Perceptual Speed, and Vocabulary). We also added a model only comprising brain-structure information not specifically acquired during performance of a cognitive task. We used a simple brain-behavioral prediction technique based on Principal Component Analysis (PCA) and regression and studied the utility of both modalities in quasi out-of-sample predictions, using split-sample simulations (= 5-fold Monte Carlo cross validation) with 1,000 iterations for which a regression model predicting a cognitive outcome was estimated in a training sample, with a subsequent assessment of prediction success in a non-overlapping test sample. The sample assignments were identical for functional connectivity, voxel activation, and brain structure, enabling apples-to-apples comparisons of predictive utility. All 3 models that were investigated included the demographic covariates age, gender, and years of education. A minimal reference model using simple linear regression with just these 3 covariates was included for comparison as well and was evaluated with the same resampling scheme as described above. Results of the comparison between voxel activation and functional connectivity were mixed and showed some dependency on cognitive outcome; however, mean differences in predictive utility between voxel activation and functional connectivity were rather small in terms of within-modality variability or predictive success. More notably, only in the case of Fluid Reasoning did concurrent functional neuroimaging provided compelling about cognitive performance beyond structural brain imaging or the minimal reference model.
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Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (October 30, 2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been used. These two methods closely resemble the human auditory system. These feature vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to investigate the nature of those acoustic features.
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Chappell, Whitney. "Phonological (in)visibility." Journal of Second Language Pronunciation 5, no. 3 (May 6, 2019): 435–63. http://dx.doi.org/10.1075/jslp.17034.cha.

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Abstract Reduced vowels between obstruents and rhotics are durationally variable and phonologically invisible in Spanish, e.g. p ə rado ‘field’ as /pɾ/. The present study compares L1-Spanish speakers, English monolinguals, and L2-Spanish learners’ perceptual boundaries for reduced vowels in Spanish. A native speaker produced 70 Spanish nonce words with word-initial obstruent + vowel + flap sequences, and the duration of each vowel was manipulated from 100% to 75%, 50%, and 25% of its original duration. To determine whether these groups perceive variably reduced vowels as phonologically visible, 78 listeners counted the number of syllables perceived in 280 target audio files. Linear regression models fitted to 21,436 responses indicate that English monolinguals apply an L1 perceptual strategy, but L2-Spanish learners have shifted their perceptual boundaries. The study concludes that the perception of highly variable acoustic information becomes more native-like with greater L2 proficiency, while age of acquisition is less predictive of native-like perception.
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Krause, Bryan M., and Geoffrey M. Ghose. "Micropools of reliable area MT neurons explain rapid motion detection." Journal of Neurophysiology 120, no. 5 (November 1, 2018): 2396–409. http://dx.doi.org/10.1152/jn.00845.2017.

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Many models of perceptually based decisions postulate that actions are initiated when accumulated sensory signals reach a threshold level of activity. These models have received considerable neurophysiological support from recordings of individual neurons while animals are engaged in motion discrimination tasks. These experiments have found that the activity of neurons in a particular visual area strongly associated with motion processing (MT), when pooled over hundreds of milliseconds, is sufficient to explain behavioral timing and performance. However, this level of pooling may be problematic for urgent perceptual decisions in which rapid detection dictates temporally precise integration. In this paper, we explore the physiological basis of one such task in which macaques detected brief (~70 ms) transients of coherent motion within ~240 ms. We find that a simple linear summation model based on realistic stimulus responses of as few as 40 correlated neurons can predict the reliability and timing of rapid motion detection. The model naturally reproduces a distinctive physiological relationship observed in rapid detection tasks in which the individual neurons with the most reliable stimulus responses are also the most predictive of impending behavioral choices. Remarkably, we observed this relationship across our simulated neuronal populations even when all neurons within the pool were weighted equally with respect to readout. These results demonstrate that small numbers of reliable sensory neurons can dominate perceptual judgments without any explicit reliability based weighting and are sufficient to explain the accuracy, latency, and temporal precision of rapid detection. NEW & NOTEWORTHY Computational and psychophysical models suggest that performance in many perceptual tasks may be based on the preferential sampling of reliable neurons. Recent studies of MT neurons during rapid motion detection, in which only those neurons with the most reliable sensory responses were strongly predictive of the animals’ decisions, seemingly support this notion. Here we show that a simple threshold model without explicit reliability biases can explain both the behavioral accuracy and precision of these detections and the distribution of sensory- and choice-related signals across neurons.
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Xue, Yuqun, Zhijiu Zhu, Jianhua Jiang, Yi Zhan, Zenghui Yu, Xiaohua Fan, and Shushan Qiao. "Fast Computation of LSP Frequencies Using the Bairstow Method." Electronics 9, no. 3 (February 26, 2020): 387. http://dx.doi.org/10.3390/electronics9030387.

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Linear prediction is the kernel technology in speech processing. It has been widely applied in speech recognition, synthesis, and coding, and can efficiently and correctly represent the speech frequency spectrum with only a few parameters. Line Spectrum Pairs (LSPs) frequencies, as an alternative representation of Linear Predictive Coding (LPC), have the advantages of good quantization accuracy and low spectral sensitivity. However, computing the LSPs frequencies takes a long time. To address this issue, a fast computation algorithm, based on the Bairstow method for computing LSPs frequencies from linear prediction coefficients, is proposed in this paper. The algorithm process first transforms the symmetric and antisymmetric polynomial to general polynomial, then extracts the polynomial roots. Associated with the short-term stationary property of speech signal, an adaptive initial method is applied to reduce the average iteration numbers by 26%, as compared to the statics in the initial method, with a Perceptual Evaluation of Speech Quality (PESQ) score reaching 3.46. Experimental results show that the proposed method can extract the polynomial roots efficiently and accurately with significantly reduced computation complexity. Compared to previous works, the proposed method is 17 times faster than Tschirnhus Transform, and has a 22% PESQ improvement on the Birge-Vieta method with an almost comparable computation time.
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Dissertations / Theses on the topic "Perceptual linear predictive"

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Wang, Yihan. "Automatic Speech Recognition Model for Swedish using Kaldi." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285538.

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With the development of intelligent era, speech recognition has been a hottopic. Although many automatic speech recognition(ASR) tools have beenput into the market, a considerable number of them do not support Swedishbecause of its small number. In this project, a Swedish ASR model basedon Hidden Markov Model and Gaussian Mixture Models is established usingKaldi which aims to help ICA Banken complete the classification of aftersalesvoice calls. A variety of model patterns have been explored, whichhave different phoneme combination methods and eigenvalue extraction andprocessing methods. Word Error Rate and Real Time Factor are selectedas evaluation criteria to compare the recognition accuracy and speed ofthe models. As far as large vocabulary continuous speech recognition isconcerned, triphone is much better than monophone. Adding feature transformationwill further improve the speed of accuracy. The combination oflinear discriminant analysis, maximum likelihood linear transformand speakeradaptive training obtains the best performance in this implementation. Fordifferent feature extraction methods, mel-frequency cepstral coefficient ismore conducive to obtain higher accuracy, while perceptual linear predictivetends to improve the overall speed.
Det existerar flera lösningar för automatisk transkribering på marknaden, menen stor del av dem stödjer inte svenska på grund utav det relativt få antalettalare. I det här projektet så skapades automatisk transkribering för svenskamed Hidden Markov models och Gaussian mixture models genom att användaKaldi. Detta för att kunna möjliggöra för ICABanken att klassificera samtal tillsin kundtjänst. En mängd av modellvariationer med olika fonemkombinationsmetoder,egenvärdesberäkning och databearbetningsmetoder har utforskats.Word error rate och real time factor är valda som utvärderingskriterier föratt jämföra precisionen och hastigheten mellan modellerna. När det kommertill kontinuerlig transkribering för ett stort ordförråd så resulterar triphonei mycket bättre prestanda än monophone. Med hjälp utav transformationerså förbättras både precisionen och hastigheten. Kombinationen av lineardiscriminatn analysis, maximum likelihood linear transformering och speakeradaptive träning resulterar i den bästa prestandan i denna implementation.För olika egenskapsextraktioner så bidrar mel-frequency cepstral koefficiententill en bättre precision medan perceptual linear predictive tenderar att ökahastigheten.
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Hrušovský, Enrik. "Automatická klasifikace výslovnosti hlásky R." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377664.

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This diploma thesis deals with automatic clasification of vowel R. Purpose of this thesis is to made program for detection of pronounciation of speech defects at vowel R in children. In thesis are processed parts as speech creation, speech therapy, dyslalia and subsequently speech signal processing and analysis methods. In the last part is designed software for automatic detection of pronounciation of vowel R. For recognition of pronounciation is used algorithm MFCC for extracting features. This features are subsequently classified by neural network to the group of correct or incorrect pronounciation and is evaluated classification success.
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Pešek, Milan. "Detekce logopedických vad v řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218106.

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The thesis deals with a design and an implementation of software for a detection of logopaedia defects of speech. Due to the need of early logopaedia defects detecting, this software is aimed at a child’s age speaker. The introductory part describes the theory of speech realization, simulation of speech realization for numerical processing, phonetics, logopaedia and basic logopaedia defects of speech. There are also described used methods for feature extraction, for segmentation of words to speech sounds and for features classification into either correct or incorrect pronunciation class. In the next part of the thesis there are results of testing of selected methods presented. For logopaedia speech defects recognition algorithms are used in order to extract the features MFCC and PLP. The segmentation of words to speech sounds is performed on the base of Differential Function method. The extracted features of a sound are classified into either a correct or an incorrect pronunciation class with one of tested methods of pattern recognition. To classify the features, the k-NN, SVN, ANN, and GMM methods are tested.
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Lin, Wei-Yen, and 林威延. "A Linear Prediction Coding On Perceptually Weighting." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/98x355.

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碩士
中原大學
電機工程研究所
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Linear prediction coefficients (LPC) analysis is a commonly used technique in speech coding. It is used to describe the short-term correlations between speech samples. Another commonly used feature for speech coding is noise-masking. A speech signal with a higher intensity will mask another one with smaller intensity. Therefore, the speech with smaller intensity is more sensitive than speech with higher intensity to noise. In this thesis, we combine the conventional LPC analysis with the noise-masking feature. In the analysis process, the difference of the estimated signal and the original signal is weighted by an A-law or μ-law like function of the original signal in the minimization of mean squared error calculations. The proposed method is used in a code excitation linear prediction (CELP) coder for simulation test. After some simulations, we find the proposed method can achieve some improvement of the speech quality. Keyword: Linear prediction, LPC, weighted LPC
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Chu, Feng-Seng, and 朱峰森. "Improved Approaches of Processing Perceptual Linear Prediction（PLP）and Mel Frequency Cepstrum Coefficient（MFCC）Parameters for Robust Speech Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/26578739886453071884.

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Books on the topic "Perceptual linear predictive"

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Nobre, Anna C. (Kia), and Gustavo Rohenkohl. Time for the Fourth Dimension in Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.036.

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This chapter takes attention into the fourth dimension by considering research that explores how predictive information in the temporal structure of events can contribute to optimizing perception. The authors review behavioural and neural findings from three lines of investigation in which the temporal regularity and predictability of events are manipulated through rhythms, hazard functions, and cues. The findings highlight the fundamental role temporal expectations play in shaping several aspects of performance, from early perceptual analysis to motor preparation. They also reveal modulation of neural activity by temporal expectations all across the brain. General principles of how temporal expectations are generated and bias information processing are still emerging. The picture so far suggests that there may be multiple sources of temporal expectation, which can bias multiple stages of stimulus analysis depending on the stages of information processing that are critical for task performance. Neural oscillations are likely to provide an important medium through which the anticipated timing of events can regulate neuronal excitability.
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Book chapters on the topic "Perceptual linear predictive"

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Kurian, Cini, and Kannan Balakrishnan. "Perceptual Linear Predictive Cepstral Coefficient for Malayalam Isolated Digit Recognition." In Communications in Computer and Information Science, 534–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24043-0_54.

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Saldanha, Jennifer C., and Malini Suvarna. "Perceptual Linear Prediction Feature as an Indicator of Dysphonia." In Lecture Notes in Electrical Engineering, 51–64. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4676-1_5.

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Trabelsi, Imen, and Med Salim Bouhlel. "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition." In Cognitive Analytics, 283–93. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch015.

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Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spectral Perceptual Linear Prediction (Rasta-PLP) features are used to characterize the emotional utterances using a combination between Gaussian mixture models (GMM) and Support Vector Machines (SVM) based on the Kullback-Leibler Divergence Kernel. In this study, the effect of feature type and its dimension are comparatively investigated. The best results are obtained with 12-coefficient MFCC. Utilizing the proposed features a recognition rate of 84% has been achieved which is close to the performance of humans on this database.
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Iwaki, Mamoru. "Information Hiding Using Interpolation for Audio and Speech Signals." In Advances in Multimedia and Interactive Technologies, 71–89. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2217-3.ch004.

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In this chapter, a time-domain high-bit-rate information hiding method using interpolation techniques, which can extract embedded data in both informed (non-blind) and non-informed (blind) ways, is proposed. Three interpolation techniques are introduced for the information hiding method, i.e., spline interpolation, Fourier-series interpolation, and linear-prediction interpolation. In performance evaluation, spline interpolation was mainly examined as an example implementation. According to the simulation of information hiding in music signals, the spline interpolation-based method achieved audio-information hiding for CD-audio signals at bit rate of about 2.9 kbps, and about 1.1 kbps under MP3 compression (160 kbps). The objective sound quality measured by the Perceptual Evaluation of Audio Quality (PEAQ) was maintained if the length of interpolation data increased. The objective sound quality was also evaluated for the Fourier series-based implementation and the linear prediction-based one. Fourier series interpolation achieved the same sound quality as spline interpolation did. Linear prediction interpolation required longer interpolation signals to get good sound quality.
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Conference papers on the topic "Perceptual linear predictive"

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Hermansky, H., and L. A. Cox. "Perceptual Linear Predictive (PLP) Analysis-Resynthesis Technique." In Final Program and Paper Summaries 1991 IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics. IEEE, 1991. http://dx.doi.org/10.1109/aspaa.1991.634094.

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Feroze, Khizer, and Abdur Rahman Maud. "Sound event detection in real life audio using perceptual linear predictive feature with neural network." In 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2018. http://dx.doi.org/10.1109/ibcast.2018.8312252.

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Shujau, M., C. H. Ritz, and I. S. Burnett. "Linear Predictive perceptual filtering for Acoustic Vector Sensors: Exploiting directional recordings for high quality speech enhancement." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947496.

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Hönig, Florian, Georg Stemmer, Christian Hacker, and Fabio Brugnara. "Revising Perceptual Linear Prediction (PLP)." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-138.

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Biswas, Arijit, and Albertus C. den Brinker. "Laguerre-Based Linear Prediction Using Perceptual Biasing." In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. IEEE, 2006. http://dx.doi.org/10.1109/acssc.2006.354928.

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Salehi, Haniyeh, and Vijay Parsa. "Nonintrusive speech quality estimation based on Perceptual Linear Prediction." In 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2016. http://dx.doi.org/10.1109/ccece.2016.7726614.

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Gamliel, Oron, and Ilan D. Shallom. "Perceptual Time Varying Linear Prediction model for speech applications." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960655.

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Motlicek, Petr, Vijay Ullal, and Hynek Hermansky. "Wide-Band Perceptual Audio Coding Based on Frequency-Domain Linear Prediction." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366667.

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Ulukaya, Sezer, and Yasemin P. Kahya. "Respiratory sound classification using perceptual linear prediction features for healthy - Pathological diagnosis." In 2014 18th National Biomedical Engineering Meeting (BIYOMUT). IEEE, 2014. http://dx.doi.org/10.1109/biyomut.2014.7026343.

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Qaisar, Saeed Mian, Noofa Hainmad, Raviha Khan, and Rawan Asfour. "A Speech to Machine Interface Based on Perceptual Linear Prediction and Classification." In 2019 Advances in Science and Engineering Technology International Conferences (ASET). IEEE, 2019. http://dx.doi.org/10.1109/icaset.2019.8714304.

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