In this paper, we present a new concept of the diierential cepstrum calculation using the FFT with interpolation in the frequency domain. The algorithm assures asymptotically exact values, without cepstral alias-ing. It completely separates the causal and the anticausal part of the cepstrum and it does not suuer from the signal singularities, i.e. zeros on the unit circle in the z-plane. The al...

We study effects of additive white noise on the cepstral representation of speech signals. Distribution of each individual cepstrum coefficient of speech is shown to depend strongly on noise and to overlap significantly with the cepstrum distribution of noise. Based on these studies, we suggest a scalar quantity, V, equal to the sum of weighted cepstral coefficients, which is able to classify f...

This paper considers the classification of radar target using Backscatter Doppler signature of moving object. Classification performance evaluated by the integrated Bispectrum based technique of feature extraction and compared it with Cepstrum based feature extraction technique. Classifier performance is tested by GMM (Gaussian Mixture Model) and ML (Maximal Likelihood) decision making method. ...

In this paper, we propose a formant weighted cepstral feature for LSP-based speech recognition system. The proposed weighting scheme is based on the well-known property of LSPs that the speech spectrum has a peak when adjacent LSFs come close. By applying this scheme to pseudo-cepstrum (PCEP) conversion process [1], we can obtain formant weighted or peak enhanced cepstral feature. Results of sp...

We propose to combine cepstrum and nonlinear time-frequency (TF) analysis to study mutiple component oscillatory signals with time-varying frequency and amplitude and with time-varying non-sinusoidal oscillatory pattern. The concept of cepstrum is applied to eliminate the wave-shape function influence on the TF analysis, and we propose a new algorithm, named de-shape synchrosqueezing transform ...

This paper addresses the environmental mismatch problem that arises from noise and channel variabilities. A new feature mapping technique based on an optimal a ne transform of the cepstrum is proposed to solve the mismatch problem observed over the speaker recognition systems. It is designed based on the fact that both the channel and noise interferences basically cause the cepstrum space to un...

A measurement system is described for acoustic absorption of automotive carpets and materials using the cepstrum technique. The system is based on prior work by Bolton [1] with modern software and hardware implementation. Time-domain averaging, minimumphase signal generation, late echo removal, and low-frequency data synthesis are implemented using commonly-available commercial hardware and sof...

Conventional cepstrums are one-dimensional, however speech characteristics are represented better by an acoustic image, a two-dimensional feature representation. In this paper, acoustic images based on two-dimensional root cepstrum (TDRC) are used as features for speaker-independent speech recognition. The TDRC is a method of feature extraction which has some advantages over other methods. The ...

This paper is concerned with the search for an optimal feature-set for a speech recognition system. A better acoustic feature analysis that suitably enhances the semantic information in a consistent fashion can reduce raw-score (no grammar) error rate sig-niicantly. A simple two-dimensional parameterized feature set is proposed. The feature-set is compared against a standard mel-cepstrum, LPC-b...

This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters f...