Cepstrum

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

Numerical and experimental analysis of a gear system with teeth defects

Abstract: The modelling of a one-stage spur gear transmission by a two degrees of freedom system produces two modes: rigid body and elastic. The time varying meshing stiffness is the main internal excitation source for the transmission and governs the behaviour of the elastic mode. Deterioration of one or several teeth, which affects the gear mesh stiffness, is considered in this work. The beginning of crack or spalling are modelled respectively by tooth having localised and distributed defect and are taken into account in the model. Simulation results are analysed by cepstrum and spectrum techniques. It is found that cepstrum and spectrum techniques are very efficient for localised and distributed defects, respectively. Series of tests are made in the experimental setup. Spectrum and cepstrum analysis of the recorded responses, with and without defects, are compared with numerical results and confirms their usefulness in gear monitoring .

Robust speech/non-speech detection using LDA applied to MFCC

Abstract: In speech recognition, speech/non-speech detection must be robust to,noise. In the paper, a method for speech/non-speech detection using a linear discriminant analysis (LDA) applied to mel frequency cepstrum coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech/non-speech detection system detects too many noise segments. The LDA applied to MFCC and the associated test reduces the detection of noise segments. This new algorithm is compared to the one based on signal to noise ratio (Mauuary and Monne, 1993).

Comparison of linear prediction cepstrum coefficients and mel-frequency cepstrum coefficients for language identification

Abstract: The speech parametrization methods: linear prediction cepstrum coefficients and mel-frequency cepstrum coefficients were compared with regard to language identification accuracy in a Gaussian mixture model based language identification system. Ten different languages were used to test against a set of ten second test files. The 12th order linear prediction cepstrum coefficients with delta and accelerate coefficients resulted in the best accuracy of 60.0 percent. This has shown that information obtained from linear prediction analysis has increased the ability of discriminating different languages. It also shows that language identification performance may be increased by encompassing temporal information by including delta and acceleration features. Besides, the performance of our test system has proved the feasibility of the modeling language by a single Gaussian Mixture Model instead of using complex system such as phonetic recogniser followed by language modelling or large vocabulary continuous speech recognition system.

Leak detection in pipelines using cepstrum analysis

Abstract: The detection and location of leaks in pipeline networks is a major problem and the reduction of these leaks has become a major priority for pipeline authorities around the world. Although the reasons for these leaks are well known, some of the current methods for locating and identifying them are either complicated or imprecise; most of them are time consuming. The work described here shows that cepstrum analysis is a viable approach to leak detection and location in pipeline networks. The method uses pressure waves caused by quickly opening and closing a solenoid valve. Due to their simplicity and robustness, transient analyses provide a plausible route towards leak detection. For this work, the time domain signals of these pressure transients were obtained using a single pressure transducer. These pressure signals were first filtered using discrete wavelets to remove the dc offset, and the low and high frequencies. They were then analysed using a cepstrum method which identified the time delay between the initial wave and its reflections. There were some features in the processed results which can be ascribed to features in the pipeline network such as junctions and pipe ends. When holes were drilled in the pipe, new peaks occurred which identified the presence of a leak in the pipeline network. When tested with holes of different sizes, the amplitude of the processed peak was seen to increase as the cube root of the leak diameter. Using this method, it is possible to identify leaks that are difficult to find by other methods as they are small in comparison with the flow through the pipe.

On-line signature verification using LPC cepstrum and neural networks

Abstract: An on-line signature verification scheme based on linear prediction coding (LPC) cepstrum and neural networks is proposed. Cepstral coefficients derived from linear predictor coefficients of the writing trajectories are calculated as the features of the signatures. These coefficients are used as inputs to the neural networks. A number of single-output multilayer perceptrons (MLPs), as many as the number of words in the signature, are equipped for each registered person to verify the input signature. If the summation of output values of all MLPs is larger than the verification threshold, the input signature is regarded as a genuine signature; otherwise, the input signature is a forgery. Simulations show that this scheme can detect the genuineness of the input signatures from a test database with an error rate as low as 4%.