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Relevant bibliographies by topics / LPC coefficients

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Journal articles
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Academic literature on the topic 'LPC coefficients'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "LPC coefficients"

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Choi, Jae-Seung. "Speaker Recognition using LPC cepstrum Coefficients and Neural Network." Journal of the Korean Institute of Information and Communication Engineering 15, no. 12 (2011): 2521–26. http://dx.doi.org/10.6109/jkiice.2011.15.12.2521.

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Olive, Joseph P. "Mixed spectral representation—Formants and LPC coefficients." Journal of the Acoustical Society of America 85, S1 (1989): S59. http://dx.doi.org/10.1121/1.2027054.

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Jung, Won-Jin, and Moo-Young Kim. "Quantization of LPC Coefficients Using a Multi-frame AR-model." Journal of the Acoustical Society of Korea 31, no. 2 (2012): 93–99. http://dx.doi.org/10.7776/ask.2012.31.2.093.

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Pérez, María Salomé, and Enrique Carrera. "LPC-based Feature Coefficients for Voice Authentication Tasks." MASKAY 2, no. 1 (2012): 73. http://dx.doi.org/10.24133/maskay.v2i1.151.

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Voice authentication is a promising biometric technique based on extracting important information from the speech signal by means of computing a vector of feature coefficients. Based on that, this paper evaluates the effectiveness of linear predictive coefficients when combined with other simple metrics in voice authentication tasks. Linear predictive coefficients were chosen due to their relatively good performance and their not-so-complicated structures when compared to other similar alternatives. All the feature coefficients have been evaluated through an extensive parameter space study in

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Hong Kook Kim, Seung Ho Choi, and Hwang Soo Lee. "On approximating line spectral frequencies to LPC cepstral coefficients." IEEE Transactions on Speech and Audio Processing 8, no. 2 (2000): 195–99. http://dx.doi.org/10.1109/89.824705.

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Sanches, I. "From LPC to normalised autocorrelation coefficients through a matrix." Electronics Letters 34, no. 4 (1998): 333. http://dx.doi.org/10.1049/el:19980310.

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Mohd Ali, Yusnita, Alhan Farhanah Abd Rahim, Emilia Noorsal, Zuhaila Mat Yassin, Nor Fadzilah Mokhtar, and Mohamad Helmy Ramlan. "Fuzzy-based voiced-unvoiced segmentation for emotion recognition using spectral feature fusions." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (2020): 196. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp196-206.

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Despite abundant growth in automatic emotion recognition system (ERS) studies using various techniques in feature extractions and classifiers, scarce sources found to improve the system via pre-processing techniques. This paper proposed a smart pre-processing stage using fuzzy logic inference system (FIS) based on Mamdani engine and simple time-based features i.e. zero-crossing rate (ZCR) and short-time energy (STE) to initially identify a frame as voiced (V) or unvoiced (UV). Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) were tested with K-nearest neighbo

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Singh, Mandeep, and Gurpreet Singh. "Word recognition from speech signal using linear predictive coding and spectrum analysis." International Journal of Engineering & Technology 7, no. 3 (2018): 1531. http://dx.doi.org/10.14419/ijet.v7i3.13285.

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This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing

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., PPS Subhashini. "TEXT-INDEPENDENT SPEAKER RECOGNITION USING COMBINED LPC AND MFC COEFFICIENTS." International Journal of Research in Engineering and Technology 03, no. 06 (2014): 508–14. http://dx.doi.org/10.15623/ijret.2014.0306095.

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Moriya, Takehiro. "Method for the modification of LPC coefficients of acoustic signals." Journal of the Acoustical Society of America 104, no. 5 (1998): 2554. http://dx.doi.org/10.1121/1.423836.

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Dissertations / Theses on the topic "LPC coefficients"

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Crosmer, Joel R. "Very low bit rate speech coding using the line spectrum pair transformation of the LPC coefficients." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/15739.

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Alvarenga, Rodrigo Jorge. "Reconhecimento de comandos de voz por redes neurais." Universidade de Taubaté, 2012. http://www.bdtd.unitau.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=587.

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Sistema de reconhecimento de fala tem amplo emprego no universo industrial, no aperfeiçoamento de operações e procedimentos humanos e no setor do entretenimento e recreação. O objetivo específico do trabalho foi conceber e desenvolver um sistema de reconhecimento de voz, capaz de identificar comandos de voz, independentemente do locutor. A finalidade precípua do sistema é controlar movimentos de robôs, com aplicações na indústria e no auxílio de deficientes físicos. Utilizou-se a abordagem da tomada de decisão por meio de uma rede neural treinada com as características distintivas do sinal de

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Kubánková, Anna. "Automatická klasifikace digitálních modulací." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-233424.

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This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method d

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Fayad, Layal. "Caractérisation de la nouvelle chambre de simulation atmosphérique CHARME et étude de la réaction d’ozonolyse d’un COV biogénique, le γ-terpinène". Thesis, Littoral, 2019. https://documents.univ-littoral.fr/access/content/group/50b76a52-4e4b-4ade-a198-f84bc4e1bc3c/BULCO/Th%C3%A8ses/LPCA/These_Fayad_Layal.pdf.

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L’étude des mécanismes et interactions atmosphériques est un des sujets majeurs actuels de recherches environnementales. La façon la plus directe et pertinente pour étudier la transformation des polluants et la formation des aérosols dans l'atmosphère est de simuler les processus dans des conditions contrôlées et simplifiées. Une nouvelle chambre de simulation CHARME (CHamber for the Atmospheric Reactivity and the Metrology of the Environment) a été développée au Laboratoire de Physico-Chimie Atmosphérique (LPCA) de l’Université du Littoral Côte d’Opale (ULCO). CHARME est également dédiée à la

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Guérin, Frédéric. "ÉMISSION DE GAZ A EFFET DE SERRE (CO2, CH4) PAR UNE RETENUE DE BARRAGE HYDROÉLECTRIQUE EN ZONE TROPICALE (PETIT-SAUT, GUYANE FRANÇAISE) :EXPÉRIMENTATION ET MODÉLISATION." Phd thesis, Université Paul Sabatier - Toulouse III, 2006. http://tel.archives-ouvertes.fr/tel-00079947.

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Les émissions de dioxyde de carbone (CO2) et de méthane (CH4) et le cycle du carbone dans la retenue de barrage de Petit-Saut et la rivière Sinnamary (Guyane Française) ont été étudiés dans le but de développer un modèle couplé hydrodynamique-biogéochimie. Le développement de ce modèle a nécessité l'étude de trois processus contrôlant ces émissions : (i) la production de CO2 et de CH4 lors de la dégradation de la matière organique (MO) des sols et de végétaux, (ii) l'oxydation aérobie du CH4 dans la colonne d'eau du barrage et (iii) les processus d'échange gazeux à l'interface air-eau.<br />Su

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Guérin, Frédéric. "Emission de gaz à effet de serre (CO2,CH4) par une retenue de barrage hydroélectrique en zone tropicale (Petit-saut, Guyane française) : expérimentation et modélisation." Toulouse 3, 2006. https://tel.archives-ouvertes.fr/tel-00079947.

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Les émissions de dioxyde de carbone (CO2) et de méthane (CH4) et le cycle du carbone dans la retenue de barrage de Petit-Saut et la rivière Sinnamary (Guyane Française) ont été étudiés dans le but de développer un modèle couplé hydrodynamique-biogéochimie. Le développement de ce modèle a nécessité l'étude de trois processus contrôlant ces émissions : (i) la production de CO2 et de CH4 lors de la dégradation de la matière organique (MO) des sols et de végétaux, (ii) l'oxydation aérobie du CH4 dans la colonne d'eau du barrage et (iii) les processus d'échange gazeux à l'interface air-eau. Sur 10

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Hong, Wei-ping, and 洪偉玶. "Usefulness of the LPC-Residue and LPC Coefficient in Text-Independent Speaker Verification." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/95586261639335208085.

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碩士<br>國立高雄第一科技大學<br>電腦與通訊工程所<br>95<br>This thesis focuses on usefulness of the LPC-Residue and LPC Coefficient in the speaker verification system. First step in the front-end feature extraction get the magnitude spectrum of the speech signal from a 32ms short-time segment of speech that is pre-emphasized and processed by a mel-scale filterbank. And the output of the filterbank is then cosine transformed to produce the cepstral coefficients. The zeroth cepstral coefficient isn’t used in the feature vector. When we gotten the coefficients, passed the coefficients to the Gaussian mixture models (

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Book chapters on the topic "LPC coefficients"

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Ratanpara, Tushar, and Narendra Patel. "Singer Identification Using MFCC and LPC Coefficients from Indian Video Songs." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13728-5_31.

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Soto-Murillo, Manuel A., Karen E. Villagrana Bañuelos, Julieta G. Rodriguez-Ruiz, et al. "Classification of Heart Health by LPC and MFCC Coefficients and Statistical Features." In IFMBE Proceedings. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30648-9_15.

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Trabelsi, Imen, and Med Salim Bouhlel. "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition." In Cognitive Analytics. 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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"Appendix A: Alternative Representations of the LPC Coefficients." In Speech Recognition Over Digital Channels. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470024720.app1.

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Aggarwal, Gaurav, and Latika Singh. "Comparisons of Speech Parameterisation Techniques for Classification of Intellectual Disability Using Machine Learning." In Research Anthology on Physical and Intellectual Disabilities in an Inclusive Society. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3542-7.ch046.

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Classification of intellectually disabled children through manual assessment of speech at an early age is inconsistent, subjective, time-consuming and prone to error. This study attempts to classify the children with intellectual disabilities using two speech feature extraction techniques: Linear Predictive Coding (LPC) based cepstral parameters, and Mel-frequency cepstral coefficients (MFCC). Four different classification models: k-nearest neighbour (k-NN), support vector machine (SVM), linear discriminant analysis (LDA) and radial basis function neural network (RBFNN) are employed for classification purposes. 48 speech samples of each group are taken for analysis, from subjects with a similar age and socio-economic background. The effect of the different frame length with the number of filterbanks in the MFCC and different frame length with the order in the LPC is also examined for better accuracy. The experimental outcomes show that the projected technique can be used to help speech pathologists in estimating intellectual disability at early ages.

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Koppula, Neeraja, K. Sarada, Ibrahim Patel, R. Aamani, and K. Saikumar. "Identification and Recognition of Speaker Voice Using a Neural Network-Based Algorithm." In Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6870-5.ch019.

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This chapter explains the speech signal in moving objects depending on the recognition field by retrieving the name of individual voice speech and speaker personality. The adequacy of precisely distinguishing a speaker is centred exclusively on vocal features, as voice contact with machines is getting more pervasive in errands like phone, banking exchanges, and the change of information from discourse data sets. This audit shows the location of text-subordinate speakers, which distinguishes a solitary speaker from a known populace. The highlights are eliminated; the discourse signal is enrolled for six speakers. Extraction of the capacity is accomplished utilizing LPC coefficients, AMDF computation, and DFT. By adding certain highlights as information, the neural organization is prepared. For additional correlation, the attributes are put away in models. The qualities that should be characterized for the speakers were acquired and dissected utilizing back propagation algorithm to a format picture.

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Kong, Weiping, Yinli Bi, Wenjiang Huang, Lingli Tang, Chuanrong Li, and Lingling Ma. "Nondestructive Evaluation of Inoculation Effects of AMF and Bradyrhizobium japonicum on Soybean under Drought Stress From Reflectance Spectroscopy." In Soybean for Human Consumption and Animal Feed. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.88673.

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Precise estimation of leaf chlorophyll content (LCC) and leaf water content (LWC) of soybean, using remote sensing technology, provides a new avenue for the nondestructive evaluation of inoculation effects of arbuscular mycorrhizal fungi (AMF) and Bradyrhizobium japonicum (BJ) on soybean growth condition. In this study, a series of pot experiments were conducted in the greenhouse, soybean inoculated with Glomus intraradices (G.i, one of AMF species), G.i and BJ, and non-inoculation were planted under drought stress (DS) and normal irrigation (NI) conditions. Leaf spectra and LCC and LWC were measured on the 28th and 56th days after inoculation. Two new simple ratio (SR) indices, derived from the first derivative spectral reflectance at λ1 nm (Dλ1) and the raw spectral reflectance at λ2 nm (Rλ2), were developed to estimate LCC and LWC. The results indicate that under DS, plants inoculated with G.i had higher LCC and LWC than the non-inoculated plants, followed by the counterparts co-inoculated with G.i and BJ. Linear estimation models, established by the D650/Rred edge and D1680/R680, achieved great improved accuracy for quantifying LCC and LWC of soybean under inoculation and drought stress treatments, with determination of coefficient of 0.63 and 0.76, respectively.

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Kaur, Taranjit, and Balwinder Singh Dhaliwal. "Design of Linear Phase FIR Low Pass Filter Using Mutation-Based Particle Swarm Optimization Technique." In Applications of Artificial Intelligence in Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2718-4.ch017.

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This chapter presents a mutation-based particle swarm optimization (PSO) approach for designing a linear phase digital low pass FIR filter (LPF). Since conventional gradient-based methods are susceptible to being trapped in local optima, the stochastic search methods have proven to be effective in a multi-dimensional non-linear environment. In this chapter, LPF with 20 coefficients has been designed. Since filter design is a multidimensional optimization problem, the concept of mutation helps in maintaining diversity in the swarm population and thereby efficiently controlling the local search and convergence to the global optimum solution. Given the filter specifications to be realized, the Mutation PSO (MPSO) tries to meet the ideal frequency response characteristics by generating an optimal set of filter coefficients. The simulation results have been compared with basic PSO and state of artworks on filter design. The results justify that the proposed technique outperforms not only in convergence speed but also in the quality of the solution obtained.

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Mahapatra, Nirmal Kumar, and Tuhin Bera. "Generalised Single-Valued Neutrosophic Number and Its Application to Neutrosophic Linear Programming." In Neutrosophic Sets in Decision Analysis and Operations Research. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2555-5.ch009.

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In this chapter, the concept of single valued neutrosophic number (SVN-Number) is presented in a generalized way. Using this notion, a crisp linear programming problem (LP-problem) is extended to a neutrosophic linear programming problem (NLP-problem). The coefficients of the objective function of a crisp LP-problem are considered as generalized single valued neutrosophic number (GSVN-Number). This modified form of LP-problem is here called an NLP-problem. An algorithm is developed to solve NLP-problem by simplex method. Finally, this simplex algorithm is applied to a real-life problem. The problem is illustrated and solved numerically.

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Srivastava, Prashant K., Swati Suman, and Smita Pandey. "Monitoring Changes in Urban Cover Using Landsat Satellite Images and Demographical Information." In Environmental Information Systems. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7033-2.ch043.

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The monitoring of urban cover is very important for the planner, management, governmental and non-governmental organizations for optimizing the use of urban resources and minimizing the environmental losses. The study here aims at analyzing the changes that occurred in urban green cover over a time span of 1991-2001 using multi-date Landsat satellite images data over the Varanasi district, India and its relation to demographical changes. The Support Vector Machines (SVMs) classifier has been used for image classification. The urbanization indicators such as Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used in order to understand the changes in urban cover and population dynamics. All the analysis indicates significant changes in the urban cover values with increasing population at both spatial and temporal scale.

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Conference papers on the topic "LPC coefficients"

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Grass, J., and P. Kabal. "Methods of improving vector-scalar quantization of LPC coefficients." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150425.

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Hyungseob Han, Sangjin Cho, and Uipil Chong. "Fault diagnosis system using LPC coefficients and neural network." In 2010 International Forum on Strategic Technology (IFOST). IEEE, 2010. http://dx.doi.org/10.1109/ifost.2010.5667999.

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Feifei, Wang, and Xu Weizhang. "A comparison of algorithms for the calculation of LPC coefficients." In 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE). IEEE, 2014. http://dx.doi.org/10.1109/infoseee.2014.6948119.

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Evans, Ward R. "A Comprative Study of the Karhunen-Loeve Transform Applied to Selected LPC Coefficients." In 1987 IEEE Military Communications Conference - Crisis Communications: The Promise and Reality. IEEE, 1987. http://dx.doi.org/10.1109/milcom.1987.4795263.

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Struwe, Kevin. "Voiced-Unvoiced Classification of Speech Using a Neural Network Trained with LPC Coefficients." In 2017 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). IEEE, 2017. http://dx.doi.org/10.1109/iccairo.2017.20.

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Ramachandran, Ravi P., M. M. Sondhi, N. Seshadri, and B. S. Atal. "Combined vector and scalar codebook for robust quantization of linear predictive coefficients (LPC) parameters." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Richard J. Mammone and J. David Murley, Jr. SPIE, 1994. http://dx.doi.org/10.1117/12.191880.

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Alshaer, Hisham, Martha Garcia, M. Hossein Radfar, Geoffrey R. Fernie, and T. Douglas Bradley. "Detection of upper airway narrowing via classification of LPC coefficients: Implications for obstructive sleep apnea diagnosis." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946495.

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Guan, C., Y. Chen, and B. Wu. "Direct modulation on LPC coefficients with application to speech enhancement and improving the performance of speech recognition in noise." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319242.

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Leinhos, Dirk C., Norbert R. Schmid, and Leonhard Fottner. "The Influence of Transient Inlet Distortions on the Instability Inception of a Low Pressure Compressor in a Turbofan Engine." In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0505.

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While studies on compressor flow instabilities under the presence of inlet distortions have been carried out with steady distortions in the past, the investigation presented here focuses on the influence of transient inlet distortions as generated by variable geometry engine intakes of super- and hypersonic aircrafts on the characteristic and the nature of the instability inception of a LPC. The flow patterns (total pressure distortion with a superimposed co- or counter-rotating swirl) of the distortions are adopted from a hypersonic concept aircraft. A LARZAC 04 twin-spool turbofan was operat

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Kubota, Shohei, Ryoichiro Yoshida, and Yoshimitsu Kuroki. "Coefficient Constraint LIC with ADMM." In 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2018. http://dx.doi.org/10.1109/iciibms.2018.8549950.

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