This paper proposes a feature extraction method named as LP_QR, based on the decomposition of the LPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired by LP_SVD and ...
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This paper proposes a feature extraction method named as LP_QR, based on the decomposition of the LPC filter impulse response matrix of the signal of interest. This feature extraction method is inspired by LP_SVD and is tested in the context of motor imagery electroencephalogram. The extracted features are classified and benchmarked against extracted features of LP_SVD method. The two applied methods are also compared regarding the required execution time, which further highlights their respective merits and demerits. This paper closely examines the contribution of EEG channels of these two information extraction algorithms too. Consequently, a detailed analysis of the role of EEG channels concerning the nature of the extracted information is presented. This study is conducted on the BCI Ilia competition database of four motor imagery movements. The obtained results indicate that the proposed method is the better choice if simplicity is demanded. The investigation into the role of EEG channels reveals that level of contribution each channel can be quite dissimilar for different feature extraction algorithms. (C) 2016 Elsevier Ltd. All rights reserved.
Acoustic phonetics is the study of the physical properties of sounds and provides means to distinguish one sound from another in quality and quantity. A study of acoustic characteristics of Kannada begins with the pho...
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ISBN:
(数字)9781538624401
ISBN:
(纸本)9781538624418
Acoustic phonetics is the study of the physical properties of sounds and provides means to distinguish one sound from another in quality and quantity. A study of acoustic characteristics of Kannada begins with the phonemic analysis of the language. Phonetic analysis of Kannada vowels is presented in this paper. The analysis of speech signal based on formant space provides a method of assessing the influence of each formant on a phoneme across gender and different age groups. PRAAT software is used for the purpose of analysis of speech signals. In this work Kannada vowels speech signals were recorded from different age groups of both male and female. Formant frequencies of corresponding vowels were computed. The analysis is carried out separately for male and female speakers. The preliminary analysis of formants of vowels show significant variations across gender and age groups. In the similar way using the linear Predictive coding (LPC) analysis is done to get in depth understanding of formants by considering different filter orders. Then order of the LPC filter is typically estimated by using information about the formants obtained using PRAAT tool.
MELP (Mixed Excitation linearprediction) is the improvement of LPC (linear Predictive coding). This is to overcome some of the limitations of LPC. It is one of speech compression and decompression algorithm. The Fede...
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ISBN:
(纸本)9781538632437
MELP (Mixed Excitation linearprediction) is the improvement of LPC (linear Predictive coding). This is to overcome some of the limitations of LPC. It is one of speech compression and decompression algorithm. The Federal standard MELP speech coder is known to provide a good quality decoded speech. The MELP Vocoder decoding part is implemented on ZYNQ-7 ZC706 using Vivado HLS and synthesized the same using the Vivado. The results of Vivado HLS and Vivado are compared.
The purpose of this paper is to improve state estimation in the event of data loss by augmenting a novel Moving Average Autoregressive-based artificial measurement vector with Kalman filtering. The proposed technique ...
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ISBN:
(纸本)9781538635674
The purpose of this paper is to improve state estimation in the event of data loss by augmenting a novel Moving Average Autoregressive-based artificial measurement vector with Kalman filtering. The proposed technique replaces the existing Autoregressive-series based model embedded in the linearprediction techniques through Moving Average Autoregressive-based model. The Autoregressive scheme needs only one type of linearprediction coefficient to be tracked, while the proposed scheme computes two parameters at each recursion. Since Autoregressive Moving Average technique possesses more information, hence it efficiently predicts the future values of a signal. This value is placed as an alternative in the structure (or steps) involved in standard process of state estimations. The ultimate consequences of this extra computations involve more computational efforts. A standard mass-spring damper case study has been provided to show some aspects of the existing and proposed techniques.
Interactions between detergents and model membranes are well described by the three-stage model: saturation and solubilization boundaries divide bilayer-only, bilayer micelle coexistence, and micelle-only ranges. An u...
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Interactions between detergents and model membranes are well described by the three-stage model: saturation and solubilization boundaries divide bilayer-only, bilayer micelle coexistence, and micelle-only ranges. An underlying assumption of the model is the equilibration of detergent between the two membrane leaflets. However, many detergents partition asymmetrically at room temperature due to slow flip-flop, such as sodium dodecyl sulfate (SDS) and lysolipids. In this work, we use isothermal titration calorimetry (ITC) and dynamic light scattering (DLS) to investigate the solubilization of unilamellar POPC vesicles by 12:0 lysophosphocholine (12:0 LPC). Flip-flop of 12:0 LPC occurs beyond the time scale of our experiments, which establish a characteristic nonequilibrated state with asymmetric distribution: 12:0 LPC partitions primarily into the outer leaflet. Increasing asymmetry stress in the membrane does not lead to membrane failure, i.e., "cracking in" as seen for alkyl maltosides and other surfactants;instead, it reduces further membrane insertion which leads to the "staying out" of 12:0 LPC in solution. At above the critical micellar concentration of 12:0 LPC in the presence of the membrane, micelles persist and accommodate further LPC but take up lipid from vesicles only very slowly. Ultimately, solubilization proceeds via the micellar mechanism (Kragh-Hansen et al., 1995). With a combination of demicellization and solubilization experiments, we quantify the molar ratio partition coefficient (0.6 +/- 0.1 mM(-1)) and enthalpy of partitioning (6.1 +/- 0.3 ***(-1)) and estimate the maximum detergent/lipid ratio reached in the outer leaflet (<0.13). Despite the inapplicability of the three-stage model to 12:0 LPC at room temperature, we are able to extract quantitative information from ITC solubilization experiments and DLS that are important for the understanding of asymmetry-dependent processes such as endocytosis and the gating of mechanosensitive channels
The delay of Phasor Measurement Unit (PMU) measurements due to the communication network may affect the performance of real time applications accommodated in a Wide Area Monitoring and Control system. In order to miti...
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ISBN:
(纸本)9781509041695
The delay of Phasor Measurement Unit (PMU) measurements due to the communication network may affect the performance of real time applications accommodated in a Wide Area Monitoring and Control system. In order to mitigate the effect of the PMU measurement delays, a data delay compensation technique based on a linear predictor is proposed in this paper. The proposed method incorporates autocorrelation linear Predictive coding (LPC) to predict the future values of the measured signals and a data delay compensator to compensate the data delays actively. The proposed data delay compensation technique is applied to the IEEE 9-bus test system where it is indicated that the proposed method can compensate the data delays in wide area signals effectively.
Isolated speech recognition system is an important step in many applications such as automated banking system, catalogue dialing, automated data entry, robotics etc. Selection of feature and classifier in the speech r...
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ISBN:
(纸本)9781467373494
Isolated speech recognition system is an important step in many applications such as automated banking system, catalogue dialing, automated data entry, robotics etc. Selection of feature and classifier in the speech recognition system is based on the complexity and recognition accuracy. Mel-frequency cepstral coefficients (MFCCs), line spectral frequencies (LSF), short time energy (STE) and linearprediction coefficients (LPC) are the features used in the existing speech recognition systems. In this paper, a sparse feature, obtained from the optimization of linearprediction coefficients (LPC) with a sparsity constraint is used for the classification. These sparse linearprediction coefficients (sparse LPC) offer a more effective way of representing the voiced speech. Artificial neural network (ANN) is used for the classification purpose. Experimental results show that the proposed method is noise robust and its performance exceeds LPC and MFCC feature based speech recognition systems.
As the hidden Markov model(HMM) has a strong ability of time sequence modeling,the continuous Gaussian mixture HMM is used to establish a model base of the rolling bearing *** adaptive particle swarm optimization(APSO...
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ISBN:
(纸本)9781510819092
As the hidden Markov model(HMM) has a strong ability of time sequence modeling,the continuous Gaussian mixture HMM is used to establish a model base of the rolling bearing *** adaptive particle swarm optimization(APSO) with extremum disturbed operator and dynamic change of inertia weights is introduced to the traditional training algorithm for solving the local extremum *** vibration signal is collected for extracting 12 order LPC coefficients as a feature vector through the dispose of adding *** the given feature vector,the HMM is built for bearing fault condition monitoring and fault ***,different fault conditions experiment are carried out on the motor bearing *** experiment result shows that the method can use a small amount of samples for training HMM,and it is more effective and has higher classification accuracy in fault diagnosis compared with the traditional training algorithm.
Audio signal modeling and simulation is important in several coding, noise removal, and recognition applications. This paper focuses on implementing models for loudness estimation and their use in estimating parameter...
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ISBN:
(纸本)9781467369985
Audio signal modeling and simulation is important in several coding, noise removal, and recognition applications. This paper focuses on implementing models for loudness estimation and their use in estimating parameters on iOS mobile devices (iPhones and iPads). We briefly address estimating excitation patterns and loudness through auditory models. These loudness estimation and other algorithms were implemented in the award winning educational iOS app iJDSP for performing DSP simulations on mobile devices. The modules were introduced to graduate students in the general signal processing area, to evaluate their effectiveness as teaching tools. The evaluation process involved giving the students a pre-quiz, guiding them through hands-on activities on the iOS app, and finally, a post-quiz. Assessments results were positive with noticeable improvement of student understanding of topics such as spectrograms and linear predictive coding.
This paper presents a real-time robust formant tracking system for speech signals and electroglottography (EGG) signals using a real-time phase equalization-based autoregressive exogenous model (RT-PEAR). PEAR can est...
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ISBN:
(纸本)9781467369985
This paper presents a real-time robust formant tracking system for speech signals and electroglottography (EGG) signals using a real-time phase equalization-based autoregressive exogenous model (RT-PEAR). PEAR can estimate formant frequencies robustly even for speech with high fundamental frequencies using phase equalization preprocessing and LPC with an impulse train. To reduce the computational complexity of original PEAR, a novel formulation of LPC with an impulse train is derived. EGG signals were used for stable detection of pitch marks since PEAR requires them. Formant estimation errors for the proposed method were less than 5 % regardless of fundamental frequencies with 12-ms processing delay. This technique will be useful for real-time speech conversion and speech-language therapy.
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