Cepstrum

About: Cepstrum is a research topic. Over the lifetime, 3346 publications have been published within this topic receiving 55742 citations.

Design of minimum-phase FIR digital filter through cepstrum

Abstract: A method to design an equiripple minimum-phase FIR filter using the cepstrum is described. This method avoids the complicated polynomial root-finding algorithm of Herrman and Schuessler (1970), or the phase-unwrapping algorithm associated with the complex cepstrum of Mian and Nainar (1982). The differential cepstrum method proposed by Pei and Lu (1986) has aliasing problems and requires the computation of three FFTs. The proposed method requires only two FFT computations and avoids the processing of phase.

Voice Activity Detection Using an Improved Unvoiced Feature Normalization Process in Noisy Environments

Abstract: Noise-elimination technology is used to eliminate noise, including environmental noise, from voice signals in order to increase voice recognition rates. Noise estimation is the most important factor in noise-elimination technology. One of the effective estimation methods is voice activity detection, which is based on the statistical properties of noise and voice. This method is a way of estimating noise using the statistical properties of both noise and voice, which have an independent Gaussian distribution. In cases of severe differences in a statistical property, like white noise, the method is very reliable but limited to signals having a low signal-to-noise ratio (SNR) or having speech shape noise, which has statistical properties similar to voice signals. Methods to increase the voice recognition rate suffer from decreasing voice recognition performance due to distortion of the voice spectrum and to missing voice frames, because noise remains if there has been incorrect estimation of the noise. Degradation in voice recognition performance emerges in the differences between the model training environment and the voice recognition environment. In order to decrease environmental discordance, various silence feature normalization methods are used. Existing silence feature normalization suffers from degradation of recognition performance because the classification accuracy for the voiced and unvoiced signals decreases by an increasing energy level in the silence section of a low SNR. This paper proposes a robust voice characteristic detection method for noisy environments using feature extraction and unvoiced feature normalization for a classification relative to the voiced and unvoiced signals. The suggested method constitutes a model for recognition by extracting the characteristics for classification of the voiced and unvoiced signals in a high SNR environment. Also, the model affects noise for voice characteristics less, and recognition performance improves by using the Cepstrum feature distribution property of voiced and unvoiced signals with a low SNR. The model was checked for its ability to improve recognition performance relative to the existing method based on recognition experiment results.

Performance analysis of speech digit recognition using cepstrum and vector quantization

Abstract: Speech recognition is a process to identify the speaker on the basis of individual information within the speech wave Recent development has made the voice recognition in the security system In this paper the implementation of speech digit recognition system is discussed This technique is mainly used in person voice identification and control access like banking by telephone, voice dialing and database access services The zero to nine digit utterances for speech data was collected The speech digit recognition mainly involves two parts, one is the feature extraction and other one is the feature matching The main approach is to isolate the speech recognition by Cepstrum and vector quantization Cepstrum technique is used for feature extraction and vector quantization is used for feature matching The result show that all digit gives good performance The proposed speech digit recognition algorithm is implemented by using MATLAB software

Multi-Channel sEMG Based Human Lower Limb Motion Intention Recognition Method*

Abstract: The paper presents a multi-channel sEMG based human lower limb motion intention recognition method, aiming at solving the problem of human lower limb motion intention recognition when using an exoskeletal robot. The cepstrum distance is used to automatically detect the endpoints of the sEMG signal for each motion. There are extracted the time domain and frequency domain characteristic parameters of the multi-channel sEMG signal, which are used to merge and constructe a joint feature matrix. The joint feature matrix is reduced by the principal component analysis (PCA) method, and a low-dimensional matrix of each motion is obtained. Traditional back propagation (BP) neural network model is optimized by the use of particle swarm optimization (PSO) algorithm. The low-dimensional matrix of each motion of the human lower limb is identified by the optimized BP neural network model. The average motion recognition rate of the improved method from 86.3%±8.24% to 93.6%±2.6% compared with the classical BP neural network algorithm in the recognition experiment. Multi-channel sEMG based human lower limb motion intention recognition method is reliable and effective.

Comparing acoustic features for robust ASR in fixed and cellular network applications

Abstract: Within the context of automatic speech recognition (ASR) applications for telephony, we investigate the acoustic preprocessing issues that are at stake in going from the fixed line to the cellular network. Because the spectral representation used in enhanced full rate GSM is linear prediction, we investigate the relative advantages and drawbacks of conventional mel-frequency cepstral coefficient (MFCC) parameters derived from a non-parametric fast Fourier transform (FFT) and MFCC parameters derived from a linear predictive coding (LPC) spectral estimate. Robust formant parameters, also derived from an LPC description of the spectrum, are studied as an alternative to MFCCs. Within the framework of connected digit recognition based on hidden Markov models, ASR performance was measured for clean conditions, as well as for three different additive noise conditions. In addition, the performance of a conventional recognition procedure was compared with the performance of an ASR system based on our acoustic backing-off implementation of missing feature theory (MFT).