Explicit expressions for the second order statistics of cepstral components representing clean and noisy signal waveforms are derived. The noise is assumed additive to the signal, and the spectral components of each process are assumed statistically independent complex Gaussian random variables. The key result developed here is an explicit expression for the cross-covariance between the log-spe...

There are several diseases that affect the human voice quality which can be organic or neurological. Acoustic analysis of voice features can be used as a complementary and noninvasive tool for the diagnosis of laryngeal pathologies. The degree of reliability and effectiveness of the discriminating process depends on the appropriate acoustic feature extraction. This work presents a parametric me...

SporTran is a Python utility designed to estimate generic transport coefficients in extended systems, based on the Green-Kubo theory of linear response and recently introduced cepstral analysis current time series generated by molecular dynamics simulations. can be applied univariate as well multivariate series. Cepstral requires minimum discretion from user, that it weakly depends two paramete...

In this paper a HTK-compatible robust speech parametrization tool CtuCopy is presented. This tool allows for the usage of several additive noise suppression preprocessing techniques, nonlinear spectrum transformation, RASTA-like filtration, and direct final feature computation. The tool is general, it is easily extendible, and it may be also used for speech enhancement purposes. In the second p...

Timbre can be defined as feature of an auditory stimulus that allows us to distinguish the sounds which have the same pitch and loudness. In this paper, we explore timbre based perceptual feature for singer identification. We start with a vocal detection process to extract the vocal segments from the sound. The cepstral coefficients, which reflect timbre characteristics, are then computed from ...

In this paper, cepstral features derived from the Differentiated Relative Higher Order Autocorrelation Sequence Spectrum (DRHOASS) are proposed for improving the robustness of a speech recognizer in the presence of background noise. Proposed method is analyzed and compared in terms of the autocorrelation coefficients they employ with the traditional feature extraction methods based on Linear Pe...

This paper proposes an integrated speech front-end for both speech recognition and speech reconstruction applications. Speech is first decomposed into a set of frequency bands by an auditory model. The output of this is then used to extract both robust pitch estimates and MFCC vectors. Initial tests used a 128 channel auditory model, but results show that this can be reduced significantly to be...

Detecting distinct features in modern pop music is an important problem that can have significant applications in areas such as multimedia entertainment. They can be used, for example, to give a visually coherent representation of the sound. We propose to integrate a singing voice detector with a multimedia, multi-touch game where the user has to perform simple tasks at certain key points in th...

Perceptual harmonic cepstral coefficients (PHCC) are proposed as features to extract for speech recognition. Pitch estimation and classification into voiced, unvoiced, and transitional speech are performed by a spectro-temporal auto-correlation technique. A peak picking algorithm is then employed to precisely locate pitch harmonics. A weighting function, which depends on the classification and ...