Cepstrum

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

Classification of closed and open shell pistachio nuts using principal component analysis of impact acoustics

Abstract: An algorithm was developed to separate pistachio nuts with closed shells from those with open shells It was observed that upon impact on a steel plate, nuts with closed shells emit different sounds than nuts with open shells Two feature vectors extracted from the sound signals were Mel cepstrum coefficients and eigenvalues obtained from the principle component analysis of the autocorrelation matrix of the signals Classification of a sound signal was done by linearly combining feature vectors from both Mel cepstrum and PCA feature vectors An important property of the algorithm is that it is easily trainable During the training phase, sounds of the nuts with closed shells and open shells were used to obtain a representative vector of each class The accuracy of closed shell nuts was more than 99% on the test set

A comparative study of formant frequencies estimation techniques

Abstract: This paper presents two techniques of formants estimation based on LPC and cepstral analysis. These methods are implemented with Matlab and applied to the problem of accurate measurement of formant frequencies. The first algorithm estimate formant frequencies from the all pole model of the vocal tract transfer function. The approach relies on the source - filter model supposing that the speech signal can be considered to be the output of a linear system. The spectral peaks in the spectrum are the resonances of the vocal tract and are commonly referred to as formants. The cepstral algorithm picks formant frequencies from the smoothed spectrum. The approach relies on decomposing the speech signal by homomorphic deconvolution into two components: the first component presents the excitation, while the second component is intended to present vocal tract resonances. The result, called cepstrum, is then used to estimate the smoothed spectrum. Formant picking is achieved by localizing the spectral maxima from the envelope. Results show the efficiency of LP based technique and the limitation of the cepstral technique in the estimation of formants of high frequencies.

Identification of hearing disorder by multi-band entropy cepstrum extraction from infant's cry

Abstract: Infant's cry is a multimodal behavior that contains a lot of information about the infant, particularly, information about the health of the infant. In this paper a new feature in infant cry analysis is presented for recognition two groups: infants with hearing disorder and normal infants, by Mel frequency multi-band entropy cepstrum extraction from infant's cry. Signal processing stage is included by silence elimination, filtering, pre-emphasizing and feature extraction. After taking Fourier transform, spectral entropy was computed as single feature for all of cry sample. In classifying stage, by training artificial neural network, correction rate of recognition was obtained 73.6%. In order to enhancement in results, we used Mel filter bank. Entropy of each sub-band constitutes elements of next feature vector. By applying Discrete Cosine Transform (DCT) over logarithm of this vector, new feature vector were obtained, we named them MFECs. By MFECs vectors we achieved 88.3% of correction rate. So, MFECs are convenient features to classify cry of infants with hearing disorder from normal infants.

Analyse des vibrations d'un engrenage : cepstre, corrélation, spectre

Abstract: This paper presents the application of cepstral analysis to the vibrations of a toothed gearing . The signal is modeled as an amplitude modulated oscillation and the effect of cepstrum is detailed . Cepstrum and autocorrelation are compared and the resolution of cepstrum is discussed .