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Hybrid MFRASTA voice recognition technology for individual's security at home
Published in Sonali Goyal, Neera Batra, N.K. Batra, An Integrated Approach to Home Security and Safety Systems, 2021
LPC is the most important feature for processing of voice signals that takes out the voice parameters such as pitch formants and spectra. This technique is used for designing user’s voice production. It predicts the future features based on previous features. It is desirable to compress the signal for efficient transmission and storage. The digital signal is compressed before transmission for efficient utilization of channels in wireless media. For medium or low bit rate coder, it is widely used. While we pass the voice signal from the voice analysis filter in order to remove the redundancy in signal, residual error is generated as an output. It can be quantized by a smaller number of bits as compared to the original signal. So now, instead of transferring the entire signal, transfer this residual error and speech parameters to generate original signal. A parametric model is computed that is based on least MSE theory, this technique is known as linear prediction. Figures 3.8 and 3.9 represent the block diagram and steps included in LPC technique.
Source Coding for Audio and Speech Signals
Published in Rajeshree Raut, Ranjit Sawant, Shriraghavan Madbushi, Cognitive Radio, 2020
Rajeshree Raut, Ranjit Sawant, Shriraghavan Madbushi
LPC is one of the most powerful and useful speech analysis techniques for encoding good quality speech at a low bit rate. It provides extremely accurate estimates of speech parameters and is relatively efficient for computation. LPC starts with the assumption that the speech signal is produced by a buzzer at the end of a tube. The glottis (space between the vocal cords) produces the buzz, which is characterized by its intensity (loudness) and frequency (pitch). The vocal tract (throat and mouth) forms the tube, which is characterized by its resonances, called as formants. LPC analyzes the speech signal by estimating formants, removing their effects from the speech signal, and estimating the speech intensity and frequency of the remaining buzz. The process of removing the formants is called inverse filtering, and the remaining signal is called residue. Because speech signal vary with time, this process is done on short chunks of speech signal, called as frames. The basic problem of LPC system is to determine the formants from the speech signal. The solution used is the difference equation, which expresses each sample of signal as a linear combination of previous sample. Such equation is called as a linear prediction, and hence the name given is Linear Prediction Coding.
Linear Prediction Vocal Tract Modeling
Published in Randy Goldberg, Lance Riek, A Practical Handbook of Speech Coders, 2019
Linear Prediction (LP) is a widely used and successful method that represents the frequency shaping attributes of the vocal tract in the source-filter model of Section 2.3. For speech coding, the LP analysis characterizes the shape of the spectrum of a short segment of speech with a small number of parameters for efficient coding. Linear prediction, also frequently referred to as Linear Predictive Coding (LPC), predicts a time-domain speech sample based on a linearly weighted combination of previous samples. LP analysis can be viewed simply as a method to remove the redundancy in the short-term correlation of adjacent samples. However, additional insight can be gained by presenting the LP formulation in the context of lossless tube modeling of the vocal tract.
Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN
Published in Journal of Information and Telecommunication, 2022
Mohammad Amaz Uddin, Refat Khan Pathan, Md Sayem Hossain, Munmun Biswas
Linear predictive coding (LPC): It is one of the most significant techniques in speech recognition (Kim, n.d.). It is also known as source filter modelling for signal processing because it has a sound source that goes through a filter and produces a signal (8). Here, is the sound source which models the vocal cords, is the filter that vocal tract and is the resulting signal. In LPC, it assumes the filter is a pth order all-pole filter with a transfer function to modelled the vocal tract transfer function shown in the following eqaution:
Classification of intellectual disability using LPC, LPCC, and WLPCC parameterization techniques
Published in International Journal of Computers and Applications, 2019
LPC analysis is carried out to characterize every sample of the speech signal in the time domain by a linear combination of p, where p is the order of the LPC analysis. In this research, LPC estimation uses the autocorrelation function of order p = 12 [22]. Frame x(n) is assumed to be 0 for n < 0 and n≥N by multiplying it with Hamming window using N = 256 point FFT. The error minimization of pth-order linear prediction yields the expression shown in Equation (2) [23,24]. Thus, the set of Equations (3) and (4) represents,
Timbre features for speaker identification of whispering speech: selection of optimal audio descriptors
Published in International Journal of Computers and Applications, 2021
Vijay M. Sardar, S. D. Shirbahadurkar
LPC is employed in speaker identification along with alternative applications like speech synthesis and speech storage. LPC represents a current speech as a linear combination of the previous samples [16]. where x(n) is that the foretold signal price, x(n − k) is that the previous sample price and, is that the predictor constant. Coefficient () is calculated by minimizing the sum of squared variations between the particular speech samples.