Predictive Coding Method Research Articles

Multi-Channel Lightweight Contrast Prediction Coding for Features Extraction of Radar Emitter Signals

This letter presents a novel multi-channel improved contrastive predictive coding (CPC) method to extract signals features. Specifically, to extract low pulse width radar signals features and meet the real-time requirements of signals identification, we design a lightweight encoder to realize the CPC features encoding function. Then, we construct a multi-channel CPC features decoder to mine and extract subtle individual signals features from the perspective of multi-domain and multi-channel information input. Simulation results verify the effectiveness of our proposed method, which can achieve state-of-the-art results in both accuracy and running time compared to the existing optimal methods. All our models and code are available at https://github.com/jn-z/MC-CPC.

COMPARISON OF CORONA DISCHARGE IDENTIFICATION IN 20 kV CUBICLES BASED ON VOLTAGE AND NOISE USING ED, HMM, AND FCM

Phenomena such as corona discharge (CD) still occurs in many electrical systems in Indonesia. As a first step for early detection of insulation failure. Identification of CD acoustic in this study namely clustering based on voltage and based on noise. So that the CD acoustic classification is set into 3 clusters. In addition, this study also classifies CD acoustic based on noise with three clusters, namely pure CD, CD with noise, and pure noise. Clustering was performed using the linear predictive coding (LPC) method as feature extraction, then a comparison of pattern matching results of feature extraction was performed using Euclidean distance (ED), hidden Markov model (HMM) and fuzzy cluster mean (FCM). The temperature in the cubical is between 27.5 ℃ - 35.3 ℃ and humidity ranges from 70% - 95%. The results of clustering accuracy on the average base voltage using the ED, HMM and FCM methods were obtained respectively 100%, 100% 93.93% for training data and 80.74%, 84.44%, 80.55% for testing data. While the results of the average base noise clustering accuracy using the ED, HMM and FCM methods were obtained respectively 100%, 100%, 94.69% for training data and 100%, 100%, 100% for testing data.

Lossless Compression Method for the Magnetic and Helioseismic Imager (MHI) Payload

The Solar Polar-orbit Observatory (SPO), proposed by Chinese scientists, is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity. However, one of the most significant challenges lies in ultra-long-distance data transmission, particularly for the Magnetic and Helioseismic Imager (MHI), which is the most important payload and generates the largest volume of data in SPO. In this paper, we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data. The background out of the solar disk is removed to decrease the pixel number of an image under compression. Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation (space, spectrum, and polarization) in data set, improving the compression ratio. Experimental results demonstrate that our method achieves an average compression ratio of 3.67. The compression time is also less than the general observation period. The method exhibits strong feasibility and can be easily adapted to MHI.

Optimization Objective Function Corona Discharge Acoustic Using Fuzzy c-Means (FcM )

In many electrical networks in Indonesia, insulation failure due to high voltage phenomena like Corona Discharge (CD) still happens. This is a result of our inability to perform early Corona Discharge (CD) identification. This study’s objective is to optimalize the sound properties of Corona Discharge (CD) as a first step throught the early identification of insulation failure in the form of clustering 20 kV cubicle. Based on observations on the needle-rod electrode 3 cm apart, the smallest breakdown was obtained at 34.3 kV. So that the classification of CD sound by 3 clusters starting 20 kV cubicle voltage until before the failure occurs on 33 kV. The temperature in the cubical is between 27.5℃ - 35.3℃ and humidity ranges from 70% - 95%. It was stated in the study that the FcM method was the most widely used and successful method. In this case, FcM can obtain more flexible results that classify data into clusters easily. This research will be carried out using the Fuzzy c-Means (FcM) method. Feature extraction with linear predictive coding (LPC) method, then optimization by using the Fuzzy c-Means (FcM) method which is expected to be used as an initial step for early detection of insulation failure.

Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods

Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds and influenza. Therefore, diagnosis of this disease and its coping and treatment approaches is also been difficult. In this study, Attempts has been made to investigate the origin of this disease and the genetic structure of the virus leading to it. For this purpose, signal processing and linear predictive coding approaches were used which are widely used in data compression. A pattern recognition model was presented for the detection and separation of covid samples from the influenza virus case studies. This model, which was based on support vector machine classifier, was tested successfully on several datasets collected from different countries. The obtained results performed on all datasets by more than 98% accuracy. The proposed model, in addition to its good performance accuracy, can be a step forward in quantifying and digitizing medical big data information.

Non-Intrusive Binaural Speech Intelligibility Prediction From Discrete Latent Representations

Non-intrusive speech intelligibility (SI) prediction from binaural signals is useful in many applications. However, most existing signal-based measures are designed to be applied to single-channel signals. Measures specifically designed to take into account the binaural properties of the signal are often intrusive – characterised by requiring access to a clean speech signal – and typically rely on combining both channels into a single-channel signal before making predictions. This letter proposes a non-intrusive SI measure that computes features from a binaural input signal using a combination of vector quantization (VQ) and contrastive predictive coding (CPC) methods. vector quantized contrastive predictive coding (VQ-CPC) feature extraction does not rely on any model of the auditory system and is instead trained to maximise the mutual information between the input signal and output features. The computed VQ-CPC features are input to a predicting function parameterized by a neural network. Two predicting functions are considered in this letter. Both feature extractor and predicting functions are trained on simulated binaural signals with isotropic noise. They are tested on simulated signals with isotropic and real noise. For all signals, the ground truth scores are the (intrusive) deterministic binaural short-time objective intelligibility (STOI). Results are presented in terms of correlations and mean-squared error (MSE) and demonstrate that VQ-CPC features are able to capture information relevant to modelling SI and outperform all the considered benchmarks – even when evaluating on data comprising of different noise field types.

SPEECH RECOGNITION APPLICATION AS AN ANIMATED OBJECT MOVEMENT CONTROLLER SYSTEM

Technological developments in the world have no boundaries. One of them is Speech Recognition. At first, words spoken by humans cannot be recognized by computers. To be recognizable, the word is processed using a specific method. Linear Predictive Coding Method (LPC) is a method used in this research to extract the characteristics of speech. The result of the LPC method is the LPC coefficient which is the number of LPC orders plus 1. The LPC coefficient is processed using Fast Fourier Transform (FFT) 512 to simplify the process of speech recognition. The results are then trained using Backpropagation Neural Network (BPNN) to recognize the spoken word. Speech recognition on the program is implemented as an animated object motion controller on the computer. The end result of this research is animated objects move in accordance with the spoken word. The optimal BPNN structure in this research is to use traingda training function, number of nodes 3, learning rate 0.05, epoch 1000, performance goal 0,00001. This structure can produce the smallest MSE value that is 0,000009957. So, this structure can recognize new words with 100% accuracy for trained data, 80% for the same respondents with trained data and reach 67.5% for new respondents.

Modeling and Reproducing Textile Sensor Noise: Implications for Textile-Compatible Signal Processing Algorithms.

Smart textiles provide an opportunity to simultaneously record various electrophysiological signals, e.g., ECG, from the human body in a non-invasive and continuous manner. Accurate processing of ECG signals recorded using textile sensors is challenging due to the very low signal-to-noise ratio (SNR). Signal processing algorithms that can extract ECG signals out of textile-based electrode recordings, despite low SNR are needed. Presently, there are no textile ECG datasets available to develop, test and validate these algorithms. In this paper we attempted to model textile ECG signals by adding the textile sensor noise to open access ECG signals. We employed the linear predictive coding method to model different features of this noise. By approximating the linear predictive coding residual signals using Kernel Density Estimation, an artificial textile ECG noise signal was generated by filtering the residual signal with the linear predictive coding coefficients. The synthetic textile sensor noise was added to the MIT-BIH Arrhythmia Database (MITDB), thus creating Textile-like ECG dataset consisting of 108 trials (30 min each). Furthermore, a Python code for generating textile-like ECG signals with variable SNR was also made available online. Finally, to provide a benchmark for the performance of R-peak detection algorithms on textile ECG, the five common R-peak detection algorithms: Pan & Tompkins, improved Pan & Tompkins (in Biosppy), Hamilton, Engelse, and Khamis, were tested on textile-like MITDB. This work provides an approach to generating noisy textile ECG signals, and facilitating the development, testing, and evaluation of signal processing algorithms for textile ECGs.

Coupling Macro-Sector-Micro Financial Indicators for Learning Stock Representations with Less Uncertainty

While the stock movement prediction has been intensively studied, existing work suffers from weak generalization because of the uncertainty in both data and modeling. On one hand, training a stock representation on stochastic stock data in an end-to-end manner may lead to excessive modeling, which involves model uncertainty. On the other, the analysis of correlating stock data with its relevant factors involves data uncertainty. To simultaneously address such uncertainty both from data and modeling perspectives, a fundamental yet challenging task is to learn a better stock representation with less uncertainty by considering hierarchical couplings from the macro-level to the sector-and micro-level. Accordingly, we propose a copula-based contrastive predictive coding (Co-CPC) method. Co-CPC first models the dependence between a certain stock sector and relevant macroeconomic variables that are sequential and heterogeneous, e.g., macro-variables are associated with different time intervals, scales, and distributions. Then, by involving a macro-sector context, stock representations are learned in a self-supervised way that can further be used for downstream tasks like stock movement prediction. Extensive experiments on two typical stock datasets verify the effectiveness of our Co-CPC method.

INDIVIDUAL IDENTIFICATION SYSTEM DESIGN THROUGH VOICE USING LINEAR PREDICTIVE CODING METHOD AND K-NEAREST NEIGHBOR

Humans have a variety of characteristics that are different from one another. Characteristics possessed by humans are genuine which can be used as a differentiator between one individual and another, one of which is sound. Voice recognition is called speech recognition. In this study, it was developed as an individual voice recognition system using a combination of the Linear Predictive Coding (LPC) method of feature extraction and K-Nearest Neighbor (K-NN) classification in the speech recognition process. Testing is done by testing changes in several parameters, namely the LPC order value, the number of frames, the K value, and different distance methods. The results of the parameter combination test showed a fairly good presentation of 73.56321839% with the combination parameter or LPC 8, the number of frames 480, the value of K 5, with the distance method used by Chebychev.

Sistem Pembelajaran Hukum Baca Al-Qur’an Menggunakan Algoritma LPC dan KNN

Seorang muslim harus dapat membaca ayat-ayat Al-Quran dengan baik sesuai yang diajarkan oleh Rasulullah saw. Membaca Al-Quran sesuai ilmu tajwid hukumnya wajib bagi setiap orang muslim, apabila seseorang membaca Al-Quran dengan tidak memakai tajwid, hukumnya berdosa. Pengembangan Aplikasi Pembelajaran Tajwid Al-Qur’an ini bertujuan untuk membantu seorang muslim agar pandai membaca al-Qur’an yang baik dan benar. Surat yang di pakai dalam aplikasi ini adalah surat Al-Fatihah. Aplikasi Pembelajaran Tajwid Al-Qur’an menggunakan Bahasa pemrograman Python dengan metode Linear Predictive Coding (LPC) sebagai ekstraksi ciri suara dan metode K-Nearest Neigbor sebagai pencocokan denan data latih. Untuk pengujian pengucapan ayat ke-1 didapatkan akurasi data sebesar 83.3%, ayat ke-2 sebesar 86.7%, ayat ke-3 sebesar 85%, ayat ke-4 sebesar 80%, ayat ke-5 sebesar 88.3%, ayat ke-6 sebesar 93.3%

Using Formants to Extract Short Vowels from Arabic Words with (Consonant Vowel)<sup>3</sup> Structure

Arabic texts suffer from missing short vowels. Arabic Speech Recognition is not as good as English speech recognition due to the short vowels not being recognized. And the Arabic language is unlike the English language in characteristics such as the number of vowels. English has more than 24 vowels that are close to each other in pronunciation. The Arabic language only has three short vowels that are far from each other in utter and measurement, by elongating those short vowels, long vowels arose. Researchers said that the vowels could be recognized using formants. The formants’ measurements of Arabic vowels are far from each other too, so it is possible to recognize them so that Arabic Speech recognition can give more accurate results. The paper applies this idea to the corpus Phonemes of Arabic. It uses the Euclidian distance method to measure the distances between formant values to recognize Arabic from words with a CV3 structure, the Linear Predictive Coding method and MATLAB to develop the programs that will extract the formants and calculate the means of the short vowels by using the corpus to identify the short vowels within words in the corpus. The results showed that if highly qualified readers were chosen to read the Arabic text, then higher rates of recognition of the short vowels involved in words will be achieved. This paper revealed that some of the characteristics of a language can be utilized for vowel recognition or to enhance the existing methods for speech recognition.

Fixed-Location Pulse Linear Prediction Coding Vocoder Model for Low Bit Rate Speech Coding

An arbitrary-location pulse determination algorithm based on multipulse linear prediction coding (MP-LPC) is presented. This algorithm can determine all the amplitudes of the pulses at a time according to given pulse locations without the use of analysis-by-synthesis. This ensures that the pulses are optimal in a least-square sense, providing the theoretical foundation to improve the quality of synthesized speech. A fixed-location pulse linear prediction coding (FLP-LPC) method is proposed based on the arbitrary-location pulse determination algorithm. Simulation of the algorithm in MATLAB showed the superior quality of the speech synthesized using pulses in different locations and processed using the arbitrary-location pulse determination algorithm. The algorithm improved speech quality without affecting coding time, which was approximately 1.5% of the coding time for MP-LPC. Pulse locations in FLP-LPC are fixed and do not need to be transmitted, with only LSF, gain, and 16 pulse amplitudes requiring coding and transmission. FLP-LPC allows the generation of synthesized speech similar to G.729 coded speech at a rate of 2.5 kbps.

Speech Recognition System with Linear Predictive Coding Method and Hidden Markov Model Using Matlab to Identify Speakers

Sistem Speech Recognition (Pengenalan Ucapan) adalah suatu proses pengenalan menggunakan MatLab yang dapat mengidentifikasi seseorang dengan mengolah suaranya. Tujuan dasar dari penelitian adalah untuk mengenali dan mengklasifikasikan ucapan-ucapan dari orang yang berbeda. Identifikasi untuk mengetahui siapa yang mengucapkan ucapan tersebut yaitu dengan cara mencocokkan karakteristik ucapan yang ada di dalam basisdata dengan ucapan masukan. Karakteristik ucapan dapat dibedakan melalui ekstraksi dengan suatu teknik pengkodean, berupa frekuensi dasar (pitch), formant dan energy. Teknik pengkodean yang umum digunakan oleh National Institute of Standar Technology (NIST) dalam peng-ekstraksian sinyal ucapan adalah LPC (Linier Predictiv Coding). Identitas unik dari setiap orang dapat dikenali menggunakan model statistik Hidden Markov Model (HMM).

Wa Language Syllables Classification by Support Vector Machine Based on Genetic Algorithm

In this paper, propose the method to classify Wa syllables by support vector machine (SVM) based on genetic algorithm (GA). Firstly, select all the Wa syllables from the speech corpus recorded. Secondly, extract pitch and formant frequencies by vowel main body extension method and linear predictive coding method respectively. Thirdly, describe every syllable with a 12-dimensional feature vector composed of its mean values, minima and maxima, belonging to its pitch frequency sequence and its first three formant frequency sequences respectively. Fourthly, divide the obtained data set into training set and test set, the former use to train SVM, and the latter test SVM. Fifthly, normalize data set into [0,1], and select the radial basis function as kernel function. Finally, use the K crossover validation method (K-CV) based on genetic algorithm (GA) to get the best parameter values train SVM. As a result, the predictive accuracy of Wa syllables classification by test set arrives at 84.83%, which has shown that the proposed method is effective and feasible on Wa syllables classification.

Mycielski Based 2d-Predictive Image Coding Algorithm

The Mycielski method is a prospering prediction algorithm which is based on searching and finding largest repeated binary patterns. It uses infinite-past data to devise a rule based prediction method on a time series. In this work, a novel two-dimensional (image processing) version of the Mycielski algorithm is proposed. Since the dimensionality definition of “past” data increases in two-dimensional signals, the proposed algorithm also needs to handle how the boundaries of the pixel cliques are iteratively extended in the neighborhood of a current pixel. The clique extension invokes novel similarity search strategies that depend on the chosen physical distance metric. The proposed prediction algorithm is used for predictive image compression and performance comparisons with other predictive coding methods are presented.

Variability of Acoustic Features of Hypernasality and it’s Assessment

Hypernasality (HP) is observed across voiced phonemes uttered by Cleft-Palate (CP) speakers with defective velopharyngeal (VP) opening. HP assessment using signal processing technique is challenging due to the variability of acoustic features across various conditions such as speakers, speaking style, speaking rate, severity of HP etc. Most of the study for hypernasality (HP) assessment is based on isolated sustained vowels under laboratory conditions. We measure the variability of acoustic features and detect HP using vowel /i/, /a/ and /u/ in continuous read speech with gradually increasing severity of HP of CP speakers. Linear predictive coding (LPC) method is used for acoustic feature extraction. In first part of our study, we observe the variation in acoustic parameters within and across vowel category with gradually increasing HP. We observe that inter-speaker variability in spectral features among CP subjects for vowel /i/ is 0.96, /a/ has 1.13 and vowel /u/ has 2.05. The inter-speaker variability measurement suggests that high back vowel /u/ is mostly affected and has the highest variability. High front vowel /i/ is least affected and has the lowest variability with HP. In the second part, ratio of vowel space area (VSA) of hypernasal and normal speech is calculated and used as a measure for HP detection. We observe that VSA spanned by CP subjects is 0.65 times less than isolated uttered Bangla nasal VSA and 0.43 times less than read speech uttered English oral VSA.

Onboard Image Processing System for Hyperspectral Sensor.

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

An Investigation of Wavelet Average Framing LPC for Noisy Speaker Identification Environment

In the presented research paper, an average framing linear prediction coding (AFLPC) method for a text-independent speaker identification system is studied. AFLPC was proposed in our previous work. Generally, linear prediction coding (LPC) has been used in numerous speech recognition tasks. Here, an investigative procedure was based on studying the AFLPC speaker recognition system in a noisy environment. In the stage of feature extraction, the speaker-specific resonances of the vocal tract were extracted using the AFLPC technique. In the phase of classification, a probabilistic neural network (PNN) and Bayesian classifier (BC) were applied for comparison. In the performed investigation, the quality of different wavelet transforms with AFLPC techniques was compared with each other. In addition, the capability analysis of the proposed system was examined for comparison with other systems suggested in the literature. In response to an achieved experimental result in a noisy environment, the PNN classifier could have a better performance with the fusion of wavelets and AFLPC as a feature extraction technique termed WFALPCF.

Research on Lossless Predictive Coding Methods for Stereo Images

There exists very high correlation between left-view images and right-view images of stereo image pairs. The correlation can help to reduce the stereoscopic image coding bit rate. Proposed methods first estimate the disparity by using causal neighborhood pixels. And then a prediction method which combines inter-view prediction with intra-view prediction is used to obtain a better predictive coding method for target-view images. The reference-view images are then encoded by using those commonly used prediction coding method. The experimental results show that compared with those methods using only intra-view correlation, the proposed method can obtain lower code rate.