Linear Prediction Technique Research Articles

A low-complexity adaptive speech dereverberation approach based on multichannel linear prediction

The adaptive multichannel linear prediction (AMCLP) technique is one of the most effective methods for speech dereverberation in real-time application systems. However, the computational complexity of the associated AMCLP algorithms is very high. In this paper, we propose a low-complexity speech dereverberation approach based on AMCLP. The coefficient matrix of the multichannel dereverberation filter is decomposed into a linear combination of a set of low-dimensional sub-filter matrices and a set of low-dimensional sub-filter vectors through Kronecker product. According to these sub-filter models, we define two sets of signal models and cost functions, from which the adaptive speech dereverberation algorithm is established. This solution not only leads to effective dereverberation performance, but also reduces the computational cost, which is very conducive to realistic speech systems.

Spectrum estimation for voiced speech using average weighted linear prediction

In many different areas, such as neurophysics, geophysics, and communication, it is vital to effectively handle signals when there is unwanted disturbance. Linear prediction (LP) methods provide precise modeling capabilities for various parametric models and are used in a wide range of fields such as predicting future data, compressing speech, analyzing spectra, filling in missing signals, and decreasing noise. LP is especially beneficial in speech processing systems. Speech analysis and synthesis greatly relies on spectral envelopes, which are closely connected to models of speech production and perception. However, the performance of the LP method may decline when limited information is involved in pitch synchronous analysis. In order to overcome this constraint, we developed the implementation of the Average Weighted Linear Prediction (AWLP) technique, which does not require the use of a window. The AWLP utilizes a signal of weighted prediction error acquired from the autocorrelation process of LP. By performing this action, it provides a more precise estimation of the power spectrum and coefficients used for vocal tract parametric models. Importantly, the effectiveness of the AWLP can be seen in both pitch synchronous and asynchronous analyses, which means it is appropriate for situations where segments are chosen randomly. We confirm the effectiveness of the AWLP technique by employing it on both synthetic voice and real speech. Additionally, we assess its effectiveness by comparing it to the direct autocorrelation technique and a modified autocorrelation (MA) approach. To sum up, the AWLP method presents a hopeful solution for reliable spectral analysis that addresses the divide between pitch synchronous and asynchronous situations.

Developing children's ASR system under low-resource conditions using end-to-end architecture

The work presented in this paper aims at enhancing the performance of end-to-end (E2E) speech recognition task for children's speech under low resource conditions. For majority of the languages, there is hardly any speech data from child speakers. Furthermore, even the available children's speech corpora are limited in terms of the number of hours of data. On the other hand, large amounts of adults' speech data are freely available for research as well as commercial purposes. As a consequence, developing an effective E2E automatic speech recognition (ASR) system for children becomes a very challenging task. One may develop an ASR system using adults' speech and then use it to transcribe children's data, but this leads to very poor recognition rates due to the stark differences in the acoustic attributes of adults' and children's speech. In order to overcome these hurdles and to develop a robust children's ASR system employing E2E architecture, we have resorted to several out-of-domain and in-domain data augmentation techniques. For out-of-domain data augmentation, we have explicitly modified adults' speech to render it acoustically similar to that of children's speech before pooling into training. On the other hand, in the case of in-domain data augmentation, we have slightly modified the pitch and duration of children's speech in order to create more data capturing greater diversity. Data augmentation approaches helps in mitigating the ill-effects resulting from the scarcity of data from child domain to a certain extent. This, in turn, reduces the error rates by a large margin. In addition to data augmentation, we have also studied the efficacy of Gamma-tone frequency cepstral coefficients (GFCC) and frequency domain linear prediction (FDLP) technique along with the most commonly used Mel-frequency cepstral coefficients (MFCC) for front-end speech parameterization. Both MFCC as well as GFCC capture and model the spectral envelope of speech. On the other hand, application of linear prediction on the frequency domain representation of speech signal helps to effectively capture the temporal envelope during front-end feature extraction. Employing FDLP features that model the temporal envelope provides important cues for the perception and understanding of stop bursts and, at times, complete phonemes. This motivated us to perform a comparative experimental study of the effectiveness of the three aforementioned front-end acoustic features. In our experimental explorations, the use of proposed data augmentation in combination of FDLP features has shown a relative improvement in character error rate by 67.6% over the baseline system. The combination of data augmentation with MFCC or GFCC features is observed to result in lower recognition performances.

Research of melting state identification and process performance based on selective laser melting acoustic signals

An acoustic signal acquisition experiment platform was constructed to gather the acoustic signals throughout the formation of 35 single-tracks of a 120 mm length copper-tin alloy in order to monitor and precisely manage the selective laser melting (SLM) forming process and enhance overall quality. The monitoring of the SLM forming process includes the analysis of the time and frequency domains, the extraction of the SLM process features using linear prediction techniques, and the development of support vector machine (SVM) model, back-propagation (BP) neural network models, and convolutional neural network models. The results show that the over-melted state can be identified by extracting time and frequency-domain features over a given range, but the normal and unmelted states are difficult to distinguish. The convolutional neural network model had a recognition rate of 99%, the BP neural network had an effective recognition rate of 90%, and the SVM model had a combined classification rate of 83.14% for the three states after optimization. In contrast, the convolutional neural network model performs best in monitoring and offers a framework and point of reference for acoustic signal analysis and online SLM quality monitoring.

Hyperspectral Image Compression Using Prediction-based Band Reordering Technique

The hyperspectral image represents various spectral properties Because it consists of broad spectral information of ground materials that can be used for various applications, These images are collected as large amounts of data that must be processed and transmitted to the ground station. These acquired images contain redundant spectral information that has to be reduced in order to reduce transmission and storage capacity. This work focuses on preserving their quality while compressing them using band reordering techniques and prediction coding. This can be accomplished by preprocessing in which sub-bands are decomposed and bands are reordered into unsequenced compression can be accomplished through using the technique of linear prediction. The report discusses the Pavia University hyperspectral image data cube, which was acquired via a sensor known as a reflected optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy.

Analysis of unit level models for small area estimation in crop statistics assisted with satellite auxiliary information

Crop statistics for a small area, such as the community development block, are an increasingly important topic in agricultural statistics. Under normality assumptions, the classic Empirical Best Linear Unbiased Prediction (EBLUP) technique is effective for predicting small area means, however the Small Area Estimation (SAE) model can be heavily affected by the incidence of outliers or deviations from the expected distribution. The purpose of this study was to estimate variance, predict block-level wheat crop yield in the Hisar and Sirsa district of Haryana by classical SAE method and a robust random-effect predictor using a slight generalization of Huber’s Proposal 2. In the case of Sirsa district, the results of classical and robust unit level SAE were very close, but not in the case of Hisar district. This could be due to the influential observation found in the Hisar data set. More accurate EBLUP wheat yield estimates are obtained when the Huber-type M-estimation method is initialized by the least square regression estimator.

Analisis Prediksi Pemilihan Mata Kuliah Peminatan pada Jurusan Teknik Informatika Universitas Indo Global Mandiri Menggunakan Metode Linier Regresi

Universitas Indo Global Mandiri (UIGM) in Palembang organizes various higher education programs, from diplomas to PhDs. In this study, we perform data analysis using linear regression methods and predictive modeling techniques. In this connection, a survey was conducted among UIGM Computer Science Engineering course students to collect the necessary data. Data generated from surveys is used to predict future student choices when choosing a course of study. According to the forecast results, 12 students choose Intelligent Computer and Vision (KCV) majors, and 84 students choose Software Engineering (RPL) majors. Due to data limitations, these predictions may not be completely accurate. However, more complete data can help improve prediction accuracy. Therefore, it is important to collect more complete and representative data to improve prediction accuracy. The more complete the data, the more accurate the forecast results, which can provide more precise guidance in decision-making about your area of ​​expertise. This study contributes to the application of linear regression and predictive modeling techniques in higher education and highlights the importance of comprehensive data collection to support better predictive outcomes.

Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction.

In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb-Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.

BUILDING RECONSTRUCTION BASED ON A SMALL NUMBER OF TRACKS USING NONPARAMETRIC SAR TOMOGRAPHIC METHODS

Abstract. Nowadays, the synthetic aperture radar (SAR) tomography (TomoSAR) technique plays a notable role in the 3D reconstruction of urban buildings through several SAR acquisitions with slightly different positions. Nonparametric-based TomoSAR spectral estimation algorithms usually work well when a large number of SAR observations. In this study, with a limited number of SAR images, we have assessed the efficiency of the nonparametric spectral estimation methods, including maximum entropy (ME), singular value decomposition (SVD), linear prediction (LP), Capon, minimum norm (MN), and beamforming (BF) in the reconstruction of the third dimension of urban buildings. The experiments are conducted on both simulated and TerraSAR-X stripmap images to indicate the effectiveness of the LP proposed estimation algorithm. The analysis of the results proves that by minimizing the average output signal power over the antenna array elements, the LP spectral estimation achieves the discrimination of distinct scatterers inside an image pixel. In addition, this low computational estimator improves the sidelobe suppression and the height estimates of the scatterers in the complex multiple-scattering urban environment. Compared to SVD, maximum entropy, Capon, minimum norm, and beamforming, the height of the Eskan tower in Tehran, Iran, obtained with the LP technique, is considerably near to field-based measurement.

Analysis of Rehabilitation Occupational Therapy Techniques Based on Instrumental Music Chinese Tonal Language Spectrogram Analysis.

This paper provides an in-depth analysis of timbre-speech spectrograms in instrumental music, designs a model analysis of rehabilitation occupational therapy techniques based on the analysis of timbre-speech spectrograms in instrumental music, and tests the models for comparison. Starting from the mechanism of human articulation, this paper models the process of human expression as a time-varying linear system consisting of excitation, vocal tract, and radiation models. The system's overall architecture is designed according to the characteristics of Chinese speech and everyday speech rehabilitation theory (HSL theory). The dual judgment of temporal threshold and short-time average energy realized the phonetic length training. Tone and clear tone training were achieved by linear predictive coding technique (LPC) and autocorrelation function. Using the DTW technique, isolated word speech recognition was achieved by extracting Mel-scale Frequency Cepstral Coefficients (MFCC) parameters of speech signals. The system designs corresponding training scenes for each training module according to the extracted speech parameters, combines the multimedia speech spectrogram motion situation with the speech parameters, and finally presents the training content as a speech spectrogram, and evaluates the training results through human-machine interaction to stimulate the interest of rehabilitation therapy and realize the speech rehabilitation training of patients. After analyzing the pre- and post-test data, it was found that the p-values of all three groups were <0.05, which was judged to be significantly different. Also, all subjects changed their behavioral data during the treatment. Therefore, it was concluded that the music therapy technique could improve the patients' active gaze communication ability, verbal command ability, and active question-answering ability after summarizing the data, i.e., the hypothesis of this experiment is valid. Therefore, it is believed that the technique of timbre-speech spectrogram analysis in instrumental music can achieve the effect of rehabilitation therapy to a certain extent.

Genotype by Environment Interaction Analysis for Grain Yield and Yield Components of Summer Maize Hybrids across the Huanghuaihai Region in China

Increasing the maize production capacity to ensure food security is still the primary goal of global maize planting. The purpose of this study was to evaluate genotypes with high yield and stability in summer maize hybrids grown in the Huanghuaihai region of China using additive main effects and multiplicative interaction (AMMI) analysis and best linear unbiased prediction (BLUP) technique. A total of 18 summer maize hybrids with one check hybrid were used for this study using a randomized complete block design (RCBD) with three replicates at 74 locations during two consecutive years (2018–2019). A three-way analysis of variance (ANOVA) and an AMMI analysis showed that genotype (G), environment (E), year (Y) and their interactions were highly significant (p &lt; 0.001) except G × E × Y for all evaluated traits viz., grain yield (GY), ear length (EL), hundred seed weight (HSW) and E × Y for hundred seed weight. The first seven interaction principal components (IPCs) were highly significant and explained 81.74% of the genotype by environment interaction (GEI). By comparing different models, the best linear unbiased prediction (BLUP) was considered the best model for data analysis in this study. The combination of AMMI model and BLUP technology to use the WAASB (weighted average of absolute scores from the singular value decomposition of the matrix of BLUP for GEI effects generated by linear mixed model) index was considered promising for similar research in the future. Genotypes H321 and Y23 had high yield and good stability, and could be used as new potential genetic resources for improving and stabilizing grain yield in maize breeding practices in the Huanghuaihai region of China. Genotypes H9, H168, Q218, Y303 and L5 had narrow adaptability and only apply to specific areas. The check genotype Z958 had good adaptability in most environments due to its good stability, but it also needs the potential to increase grain yield. Significant positive correlations were also found between the tested agronomic traits.

Delta robot joints control-based linear MPC controller

Controlling the joints' angles of a robot is an important step which lead to controlling the robot end effector position and/or speed. Thus, it has been a vast area of interest in research which has good investigating potentials using several control types such as classical, modern and optimal control methods. In this work, a linear model predictive control MPC technique was proposed to control the joints' angle of a three degree of freedom delta robot. The inverse kinematics, direct kinematics, and dynamic model of the robot were analysed. Then, the dynamic model represented in a linearised around an operating point state space model. In order to investigate the performance of the proposed MPC controller a simulator-based MATLAB program was implemented. The simulation results have showed the efficiency of the proposed controller in the joints' angles control problem. This illustrates that the MPC controller can derive the joints' angles to track the desired angles with invisible steady state error.

DEVELOPMENT OF ACADEMIC ATTENDANCE SYSTEM USING VOICE VERIFICATION

Based on the fact that all Human Voices are different, where every individual’s voice contains unique characteristics that can be distinguished from others by using special analysis, voice verification can also be used for biometric recognition of individual. The aim of this project is to develop an automated attendance process captivating and student databases record maintaining using C++ programming and voice verification technique. The Input speech will undergo a series of voice recognition processes. The process starts with Feature extraction to obtain the attribute of voice. The features or specific characteristics for each voice are extracted by linear predictive coding technique (LPC) into feature vectors which later be used in speaker modeling. In speaker modeling stage the feature vectors of each speaker will be then processed by Gaussian Mixture Modeling (GMM) technique to generate speaker models and will be stored in a database. Verification process is done through template matching technique. Based on log-likelihood logic decision (template matching technique) with respect to the FAR (False Acceptance Rate) and FRR (False Rejection Rate), the identity of that speaker is accepted if the match is above the threshold. Once the verification result is accepted, the attendance in MySQL database for that speaker will be auto-updated.

Agro Expert Using Google API

Agriculture is the backbone of Indian economy. Mostly crops are loss due to the erroneous selection of the crop, climate change. The farmers are generally not aware of the requirements of the crops. i.e. The minerals, soil moisture &amp; other soil requirements one more problem that a farmer generally encounters is the pest &amp; diseases that can affect the crops they grow which they are generally unaware of in an early stage. This are the problems of farmer is addressed in our paper and we have tried to solve it with the help of Agro Expert using Google API system by the help of our model. We predict the suitable crop to the farmers and also detect the pest and may affect as suggest the pest control technique. In this paper we have applied collaborative filtering algorithm, aprori algorithm, linear regration prediction technique.

Listening Enhancement in Noisy Environments: Solutions in Time and Frequency Domain

The intelligibility of speech from a telephone or a public address system is often affected by acoustical background noise in the near-end listening environment. Speech intelligibility and listening effort can be improved by adaptive pre-processing of the loudspeaker signal. This is called Near-End Listening Enhancement (NELE). The speech spectrum is dynamically modified, taking the acoustical background noise at the near-end into account. In this paper, two opposite NELE strategies with either Noise-Masking-Proportional Shaping or Noise-Masking-Inverse Shaping are proposed which are appropriate for different noise characteristics. Both strategies are formulated in closed form in the frequency domain. They do not require to optimize an intelligibility measure but use explicitly the masking threshold. Motivated by the frequency domain approach, a simpler time domain solution is derived which is based on linear prediction techniques and does not need the masking calculations. The proposed NELE solutions outperform state-of-the-art in terms of computational complexity, memory requirement, continuous processor load, and latency.

Wavefront prediction using artificial neural networks for open-loop adaptive optics

ABSTRACT Latency in the control loop of adaptive optics (AO) systems can severely limit performance. Under the frozen flow hypothesis linear predictive control techniques can overcome this; however, identification and tracking of relevant turbulent parameters (such as wind speeds) is required for such parametric techniques. This can complicate practical implementations and introduce stability issues when encountering variable conditions. Here, we present a non-linear wavefront predictor using a long short-term memory (LSTM) artificial neural network (ANN) that assumes no prior knowledge of the atmosphere and thus requires no user input. The ANN is designed to predict the open-loop wavefront slope measurements of a Shack–Hartmann wavefront sensor (SH-WFS) one frame in advance to compensate for a single-frame delay in a simulated 7 × 7 single-conjugate adaptive optics system operating at 150 Hz. We describe how the training regime of the LSTM ANN affects prediction performance and show how the performance of the predictor varies under various guide star magnitudes. We show that the prediction remains stable when both wind speed and direction are varying. We then extend our approach to a more realistic two-frame latency system. AO system performance when using the LSTM predictor is enhanced for all simulated conditions with prediction errors within 19.9–40.0 nm RMS of a latency-free system operating under the same conditions compared to a bandwidth error of 78.3 ± 4.4 nm RMS.

VLSI Implementation of Lossless ECG Compression Algorithm for Low Power Devices

This brief presents a VLSI implementation of an efficient lossless compression scheme for electrocardiogram (ECG) data encoding to save storage space and reduce transmission time. As compression algorithm is able to save storage space and reduce transmission time, this opportunity has been seized by implementing memory-less design while working at a high clock speed in VLSI. ECG compression algorithm comprises two parts: an adaptive linear prediction technique and content-adaptive Golomb Rice code. An efficient and low power VLSI implementation of compression algorithm has been presented. To improve the performance, the proposed VLSI design uses bit shifting operations as a replacement for the different arithmetic operations. VLSI implementation has been applied to the MIT-BIH arrhythmia database which is able to achieve a lossless bit compression rate of 2.77. Moreover, VLSI architecture contains 3.1 K gate count and core of the chip consumes 27.2 nW of power while working at 1 KHz frequency. The core area is 0.05 mm2 in 90 nm CMOS process.

A Cross-Layer Routing Metric with Link Prediction in Wireless Mesh Networks

Abstract Wireless Mesh Networks (WMNs) is one of the encouraging technologies for future-generation wireless network with diverse applications as it is undergoing rapid progress. WMNs are multi-hop networks which are beneficial in situations where there is little or no network infrastructure. WMNs consist of mesh nodes which interact with each other to establish a network connection. Mesh nodes include mesh clients and mesh routers. Mesh clients can be stationary or move from one place to another, whereas mesh routers have minimum movability and act as a backbone for WMNs. Due to the mobility of mesh nodes, the fundamental problem in WMNs is estimating the link quality. The efficiency of network protocols depends on the accuracy of link quality. Therefore, there is a need for an intelligent mechanism to find solutions to link quality problems in the wireless network. To address this problem, we apply machine learning prediction techniques. We evaluate the performance of prediction techniques like multiple linear regression, support vector regression and Gaussian regression. The experiments were conducted on various scenarios with cross-layer routing metric PCL-IDA to predict the link quality, which is embedded in OLSR routing protocol. Results using NS-2 simulations reveal that the link quality parameters throughput, average delay and packet loss are better when multiple linear regression prediction technique is applied.

Widely linear denoising of multicomponent seismic data

ABSTRACTSeismic data processing is a challenging task, especially when dealing with vector‐valued datasets. These data are characterized by correlated components, where different levels of uncorrelated random noise corrupt each one of the components. Mitigating such noise while preserving the signal of interest is a primary goal in the seismic‐processing workflow. The frequency‐space deconvolution is a well‐known linear prediction technique, which is commonly used for random noise suppression. This paper represents vector‐field seismic data through quaternion arrays and shows how to mitigate random noise by proposing the extension of the frequency‐space deconvolution to its hypercomplex version, the quaternion frequency‐space deconvolution. It also shows how a widely linear prediction model exploits the correlation between data components of improper signals. The widely linear scheme, named widely‐linear quaternion frequency‐space deconvolution, produces longer prediction filters, which have enhanced signal preservation capabilities shown through synthetic and field vector‐valued data examples.

Power flow in heterogeneous battery systems

• Control framework of microgrids can be used for power flow control. • Power flow control can be achieved with static, dynamic or optimisation based methods. • System performance, efficiency and service life are essential optimisation objetives. • Optimisation based methods provide flexibility to the operator. Control methods are important for stationary energy systems, especially for those based on so called second life batteries, due to asymmetrical system design and a mix of batteries from different capacities and aging levels. This study provides a theoretical analysis and simulation results of power flow and control methods in stationary energy storage systems. The theoretical part of this work develops a hierarchical control scheme comprising several levels and methods for computing the target variables of the control strategy based on optimisation. The simulation study focuses on the implementation of a stationary energy storage system, comprising four BMW i3 battery units connected to four DC/DC converters, and three DC/AC inverters. For this system four control strategies are developed in detail and compared. Three algorithms are based on feedback through a finite state machine, while the fourth uses linear programming and model predictive control techniques developed in this work. All methods are tested on two application profiles (artificial rectangle profile, frequency regulation profile) and the control schemes are compared in terms of performance, efficiency, and service life. The results suggest that optimisation based methods provide the flexibility to design a control strategy that weighs the three control objectives.