Cepstrum

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

Mitigating the effects of granular scattering using cepstrum analysis in terahertz time-domain spectral imaging

Abstract: Terahertz (THz) imaging is a widely used technique in the study and detection of many chemicals and biomolecules in polycrystalline form because the spectral absorption signatures of these target materials often lie in the THz frequencies. When the size of dielectric grain boundaries are comparable to the THz wavelengths, spectral features can be obscured due to electromagnetic scattering. In this study, we first investigate this granular scattering effect in identification of chemicals with THz spectral absorption features. We then will propose a signal processing technique in the so-called "quefrency" domain to improve the ability to resolve these spectral features in the diffuse scattered THz images. We created a pellet with α-lactose monohydrate and riboflavin, two biologically significant materials with well-known vibrational spectral resonances, and buried the pellet in a highly scattering medium. THz transmission measurements were taken at all angles covering the half focal plane. We show that, while spectral features of lactose and riboflavin cannot be distinguished in the scattered image, application of cepstrum filtering can mitigate these scattering effects. By employing our quefrency-domain signal processing technique, we were able to unambiguously detect the dielectric resonance of lactose in the diffused scattering geometries. Finally we will discuss the limitation of the new proposed technique in spectral identification of chemicals.

A new voice transformation method based on both linear and nonlinear prediction analysis

Abstract: We describe a voice transformation method which changes the source speaker's acoustic features to those of a target speaker. In the method acoustic features are divided into two parts, linear and nonlinear parts. Linear parts are characterized by LPC cepstrum coefficients which are obtained from LP analysis. The nonlinear part, which represents the excitation signal, is modelled by the long-delay nonlinear predictor using a neural net. Conversion rules for the excitation signal are generated by the average pitch ratio and the mapping codebook, and those for LPC cepstrum coefficients are based on the orthogonal vector space conversion. In addition, the spectral envelope compensation is proposed to correct spectral distortion. In the transformed speech a listening test shows that the proposed method makes it possible to convert speaker's individuality while maintaining high quality.

Iterative cepstrum-based approach for speech dereverberation

Abstract: This paper presents a novel approach for dereverberation of speech signals, based on an iterative cepstral separation of the non-minimum phase room response. The algorithm performs an iterative flattening of the room transfer function (RTF) magnitude spectrum prior to suppression of phase distortion. For ill-conditioned inversion problems, the proposed method is superior to the standard homomorphic techniques, as it overcomes the loss in computational efficiency inflicted by the long time responses of the inverse filters. By using the fast-decaying sequences shorter than the length of a full inverse, the finite-point nature of a discrete Fourier transform (DFT) is made noncritical. Results with measured room responses conform with the theory presented in the paper.