EEG Signal Classification using Linear Predictive Cepstral Coefficient Features
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An electroencephalogram (EEG) is a procedure that records brain wave patterns, which are used to identify abnormalities related to the electrical activities of the brain.In this study an effective algorithm is proposed to automatically classify EEG clips into two different classes: normal and abnormal.For categorizing the EEG data, feature extraction techniques such as linear predictive coefficients (LPC) and linear predictive cepstral coefficients (LPCC) are used.Support vector machines (SVM) is used to classify the EEG clip into their respective classes by learning from training data.Keywords:
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Cepstrum
Mel-frequency cepstrum
This document belongs to the implementation of a system of recognition of the bases on the Hidden Models of Markov HMM. Due to a system of recognition, we will use the techniques of parameterization which handles the mechanisms of the human ear Mel Frequency cepstral coefficient MFCC and Perceptual linear prediction PLP starting from the database TIMIT. We also used two indices Jitter and Shimmer since they show very precise information about the voice of the person.
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Four automatic speaker recognition techniques were investigated with a contain speech data base to determine their effectiveness in a text independent mode. These four techniques used the correlation of short and long term spectral averages, cepstral measurements of long term spectral averages, orthogonal linear prediction of the speech waveform, and long term average LPC reflection coefficients combined with pitch and overall power. The results of this study indicate that LPC derived parameters perform better than do those derived from cepstral and spectral data. Recognition accuracies of 95% and 93% were obtained for LPC based techniques with 13 seconds of unknown speech. The corresponding recognition accuracies for the cepstral and spectral based systems were 79% and 54% respectively.
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This paper is to compare two most common features representing a speech word for speech recognition on the basis of accuracy, computation time, complexity and cost. The two features to represent a speech word are the linear predict coding cepstra (LPCC) and the Mel-frequency cepstrum coefficient (MFCC). The MFCC was shown to be more accurate than the LPCC in speech recognition using the dynamic time warping method. In this paper, the LPCC gives a recognition rate about 10% higher than the MFCC using the Bayes decision rule for classification and needs much less computational time to be extracted from speech signal waveform, i.e., the MFCC needs computational time 5.5 time as much as the LPCC does. The algorithm to compute a LPCC from a speech signal much simpler than a MFCC, which has many parameters to be adjusted to smooth the spectrum, performing a processing that is similar to be adjusted to smooth the spectrum, performing a processing that is similar to that executed by the human ear, but the LPCC is easily obtained by the least squares method using a set of recursive formula.
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Linear prediction
Dynamic Time Warping
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Mel-frequency cepstrum coefficients (MFCC) have been the most popular features used in speaker recognition. It has been recently shown that residual signal estimated through linear prediction (LP) also conveys speaker-specific information, and applied to speaker identification. In this paper, we investigate on the impact of LP-residual cepstrum coefficients (LPRC) on speaker verification along with MFCC and linear predictive cepstrum coefficients (LPCC) as well, and make comparisons of their performance in verification by conducting experiments on NIST 2001 SRE corpus, including modern classifiers. It is shown that LPRC features are as useful as MFCC and LPCC features in speaker verification, and fusing the LPRC, LPCC, and MFCC features in pairs improves the verification performance.
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NIST
Speaker Verification
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Abstract Speech signal is redundant and non-stationary by nature. Because of vocal tract inertness these variations are not very rapid and the signal can be considered as stationary in short segments. It is presumed that in short-time magnitude spectrum the most distinct information of speech is contained. This is the main reason for speech signal analysis in frame-by-frame manner. The analyzed speech signal is segmented into overlapping segments (so-called frames) for this purpose. Segments of 15-25 ms with the overlap of 10-15 ms are used usually. In this paper we present results of our investigation of analysis window length and frame shift influence on speech recognition rate. We have analyzed three different cepstral analysis approaches for this purpose: mel frequency cepstral analysis (MFCC), linear prediction cepstral analysis (LPCC) and perceptual linear prediction cepstral analysis (PLPC). The highest speech recognition rate was obtained using 10 ms length analysis window with the frame shift varying from 7.5 to 10 ms (regardless of analysis type). The highest increase of recognition rate was 2.5 %.
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Mel-frequency cepstrum
Linear prediction
SIGNAL (programming language)
Vocal tract
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In this paper we introduce a new set of features that provides improved performance for speaker identification. This feature set is referred to as the adaptive component weighting (ACW) cepstral coefficients. The ACW scheme modifies the linear predictive (LP) spectral components (resonances) so as to emphasize the formant structure by attenuating the broad-bandwidth spectral components. Such components are found to introduce undesired variability in the LP spectra of speech signals due to environmental factors. The ACW cepstral coefficients represent an adaptively weighted version of the LP cepstrum. The adaptation results in deemphasizing the irrelevant variations of the LP cepstral coefficients on a frame-by-frame basis. Experiments are presented using the San Diego portion of the King database. The ACW cepstrum is shown to offer improved speaker identification performance as compared to other common methods of cepstral weighting.< >
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Speaker diarisation
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본 논문에서는 퍼셉트론 신경회로망과 선형예측부호화 켑스트럼 계수를 사용한 화자인식 알고리즘을 제안한다. 제안하는 화자인식 알고리즘은 입력받은 음성신호에 대해서 유성음 구간을 추출한다. 추출된 유성음 구간에 대하여 선형예측 분석에 의하여 화자의 특성을 가지고 있는 선형예측부호화 켑스트럼 계수를 구한다. 구해진 선형예측부호화 켑스트럼 계수를 분류하기 위하여 이 켑스트럼 계수를 퍼셉트론 신경회로망의 입력으로 사용하여 네트워크의 학습을 수행한다. 본 실험에서는 선형예측부호화 켑스트럼 계수와 신경회로망을 사용하여 본 화자인식 알고리즘이 유효하다는 것을 인식률을 통하여 확인한다. This paper proposes a speaker recognition algorithm using a perceptron neural network and LPC (Linear Predictive Coding) cepstrum coefficients. The proposed algorithm first detects the voiced sections at each frame. Then, the LPC cepstrum coefficients which have speaker characteristics are obtained by the linear predictive analysis for the detected voiced sections. To classify the obtained LPC cepstrum coefficients, a neural network is trained using the LPC cepstrum coefficients. In this experiment, the performance of the proposed algorithm was evaluated using the speech recognition rates based on the LPC cepstrum coefficients and the neural network.
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Mel-frequency cepstrum
Linear prediction
Multilayer perceptron
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Cepstrum
Mel-frequency cepstrum
Linear prediction
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The focus of the article is on the selection, adjustment and overall performance of speech features at acoustical and prosodic level for speaker recognition task. Namely: perceptual linear prediction, Mel frequency cepstra, cepstral linear prediction, formant frequencies, and different auxiliary features. Both brief theoretical backgrounds and possible computational methods are outlined in regard to the speaker recognition task. In the series of experiments using 114 speakers database, it was observed that a model based method slightly outperformed the perceptual ones. Furthermore, it was found that auxiliary and prosodic features may not always improve scores when processed together with acoustic ones. On average the success rate was about 90% whereas the best recorded score was 99.1% for cepstral linear prediction coefficients in connection with k-nearest neighbor classifier.
Linear prediction
Mel-frequency cepstrum
Cepstrum
Speaker identification
Speaker diarisation
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