New Robust LPC-Based Method for Time-resolved Morphology of High-noise Multiple Frequency Signals

Speech recognition can be defined as the process of converting voice signals into the ranks of the word, by applying a specific algorithm that is implemented in a computer program. The research of speech recognition in Indonesia is relatively limited. This paper has studied methods of feature extraction which is the best among the Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCC) for speech recognition in Indonesian language. This is important because the method can produce a high accuracy for a particular language does not necessarily produce the same accuracy for other languages, considering every language has different characteristics. Thus this research hopefully can help further accelerate the use of automatic speech recognition for Indonesian language. There are two main processes in speech recognition, feature extraction and recognition. The method used for comparison feature extraction in this study is the LPC and MFCC, while the method of recognition using Hidden Markov Model (HMM). The test results showed that the LPC method is better than MFCC in Indonesian language speech recognition.

The aims of Brain-Computer interfaces (BCI) research is helping paralyzed people communicating with others by using their electroencephalogram (EEG) signals. In this study, EEG signals from 5 mental tasks were recorded from 7 subjects and combinations of 2 different mental tasks were studied for each subject for one trial. The motivation for this work is using Linear predictive Coding (LPC) method to compress channels of EEG one channel. Eight features are employed for each signal of EEG using LPC 1st order followed by 3 level Discrete Wavelet Transform (DWT). New mother wavelet is used to be near the waveform of EEG signals. Statistical calculations are conducted for the 4 coefficients of DWT. Classification is conducted using support vector machine SVM. The classifier using SVM provided a high recognition rate reaching up to 100%, in some cases, and an average rate of about 85 %. The average specificity percent is 83.33 %. The average sensitivity percent is 86.66%.

본 논문은 유도전동기의 고장검출 및 진단을 위한 효율적인 2-단계 고장예측 알고리즘을 제안한다. 첫 번째 단계에서는 고장 패턴 추출을 위해 선형 예측 부호화 (Linear Predictive Coding: LPC) 기법을 사용하고, 두 번째 단계에서는 고장 패턴 매칭을 위해 동적시간교정법 (Dynamic Time Warping: DTW)을 사용한다. 유도전동기에서 정상 및 각종 이상 상태의 조건을 발생시켜 추출한 샘플링 주파수 8kHz, 샘플링 시간 2.2초의 정상상태 및 비정상 상태의 진동데이터 8개를 사용하여 모의 실험한 결과, 제안한 고장예측 알고리즘은 기존의 고장진단 알고리즘보다 약 45%의 정확도 향상을 보였다. 또한 TI사의 TMS320F2812 DSP를 내장한 테스트베드 시스템을 제작하여 제안한 고장예측 알고리즘을 구현하고 검증하였다. This paper proposes an efficient two-stage fault prediction algorithm for fault detection and diagnosis of induction motors. In the first phase, we use a linear predictive coding (LPC) method to extract fault patterns. In the second phase, we use a dynamic time warping (DTW) method to match fault patterns. Experiment results using eight vibration data, which were collected from an induction motor of normal fault states with sampling frequency of 8 kHz and sampling time of 2.2 second, showed that our proposed fault prediction algorithm provides about 45% better accuracy than a conventional fault diagnosis algorithm. In addition, we implemented and tested the proposed fault prediction algorithm on a testbed system including TI's TMS320F2812 DSP that we developed.

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.

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.

Acoustical Comparison Between Samples of Good and Poor Vibrato in Singers

Human Voice Signal Synthesis and Coding

A single chip solution for text-to-speech synthesis is presented in this paper. The proposed system is the first hardware solution for synthesizing unlimited vocabulary speech in real time for Turkish language. The integrated circuit converts incoming letters in ASCII format to speech by using clues from the textual context. The system has a language dependent database, which contains pre-recorded human speech samples coded by LPC (linear predictive coding) method. It has a very low complexity (consisting of only 3503 technology specific cells) compared to other board-level designs available in the market, resulting in low power and flexibility for FPGA implementation or incorporation into larger chips as IP.

Optimizing genomic signal extraction of COVID-19 variants using the multi linear predictive coding (M-LPC) method

We are investigating countermeasure techniques against electric noise on telecommunication and related equipment. There are many types of electric noise, and we focus on the characterization of transient noise such as the switching noise of switching power supplies. We are developing a method of transient signal analysis based on linear predictive coding (LPC) for the purpose. Unfortunately, as standard LPC contains Yule-Walker (YW) approximation, which replaces a given time series with a periodic time series for simplification, it is not suitable for transient signal analysis. Therefore, our method of transient signal analysis does not contain YW approximation. Instead, we use the local linearization technique, which was developed in the field of quantum mechanics. Our method makes it possible to obtain instantaneous frequencies and instantaneous decay rates simultaneously with high precision from a small number of samples. We discuss our LPC method avoid using YW approximation and using the local linearization technique and its application to repetitive ringing signals, which mimics the switching noise of a switching power supply, to show the efficiency of our method.

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Sensor data validity is extremely important in a number of applications, particularly building technologies. An example of this is Oak Ridge National Laboratory's ZEBRAlliance research project, which consists of four single-family homes located in Oak Ridge, TN. The homes are outfitted with a total of 1,218 sensors to determine the performance of a variety of different technologies integrated within each home. Issues arise with such a large amount of sensors, such as missing or corrupt data. This paper aims to eliminate these problems using: (1) Kalman filtering and (2) linear predictive coding (LPC) techniques. Simulations show the Kalman filtering method performed best in predicting temperature, humidity, pressure, and airflow data, while the LPC method performed best with energy consumption data.

Chapter 10 - Parameterization techniques for automatic speech recognition system

From the Publisher: A great deal of modern communications equipment is being converted from analog to digital technology. This timely book explains many of the important concepts related to digital signal processing in easy-to-understand discussions of communications techniques, data transmission, filters, and hardware. Readers are given practical information on how to apply theory and algorithms to the design of radio receivers and transmitters. Among the areas discussed are analog to digital conversion - with emphasis on noise and distortion performance; manipulation of complex signals - positive and negative frequencies, plus Hilbert transformers; digital filters - guidelines for performance in communications, plus decimation and interpolation; hardware - multiplier accumulators, fast Fourier transform processors, digital signal processors, data flow techniques in equipment, and hardware simulation and testing; and speech processing - linear predictive coding (LPC), code excited linear predictive coding (CELP), and how to digitize speech at low data rates. Development of algorithms for oscillators, detectors, modulators, automatic gain control circuits, and other devices is clearly explained. Specific algorithms are provided for AM modulation, frequency modulation, FM detection, threshold extension, audio compression, automatic gain control, and squelch circuitry. Explanations of basic concepts of digital signal processing and data transmission are accompanied by reviews of signal representations, sampling, convolution, and z-transforms. Extensive real-world examples contribute to expertise in many facets of incorporating digital technology into devices. This hands-on treatment of DSP will help communications engineers upgrade their skills in digital signal processing and make a smooth transition into the design of more advanced systems. It also meets the needs of students who want to bolster their knowledge in communications.

In many practical applications, signals to be detected are unknown nonlinear frequency modulated (FM) and are corrupted by strong noise. The phase histories of signals are assumed to be unknown smooth functions of time and these functions are poorly modeled or unmodeled by a small number of parameters. Thus, the conventional parametric-based detection methods are invalid in these cases. This paper proposes a nonparametric detection method using the ridge energy of observations. The detection process consists of three steps, TF ridge detection, ridge energy extraction, and decision. First, the directionally smoothed-pseudo-Wigner-Ville distribution (DSPWVD) is introduced to highlight the instantaneous frequency (IF) points along a special direction on the IF curve of a signal from noise. Further, an angular maximal distribution (AMAD) is constructed from a set of DSPWVDs to highlight the entire IF curve. As a result, the TF ridge of an observation can be estimated well from its AMAD by the maxima position detector. Second, the ridge energy, the total energy along the TF ridge on the pseudo-Wigner-Ville distribution (PWVD), is extracted. A noisy signal has larger ridge energy than a pure noise does, with a large probability, because pure noise energy is randomly distributed throughout the TF plane while the signal energy in a noisy signal is concentrated along the estimated TF ridge. Third, the ridge energy of an observation is used as the test statistic to decide whether or not a signal of interest is present in the observation, where the decision threshold is determined by a large number of Monte Carlo simulations using pure noise. Finally, the simulation experiments to two test signals are made to verify the effectiveness of the proposed method.