cepstrum analysis

Most video processing applications require object tracking as it is the base operation for real-time implementations such as surveillance, monitoring and video compression. Therefore, accurate tracking of an object under varying scene conditions is crucial for robustness. It is well known that illumination variations on the observed scene and target are an obstacle against robust object tracking causing the tracker lose the target. In this paper, a 2D-cepstrum based approach is proposed to overcome this problem. Cepstral domain features extracted from the target region are introduced into the covariance tracking algorithm and it is experimentally observed that 2D-cepstrum analysis of the target object provides robustness to varying illumination conditions. Another contribution of the paper is the development of the co-difference matrix based object tracking instead of the recently introduced covariance matrix based method.

In this paper, we examine how energy computation and filterbank design contribute to the overall front-end robustness, especially when the investigated features are applied to noisy speech signals, in mismatched training-testing conditions. In prior work ("Auditory Teager energy cepstrum coefficients for robust speech recognition," D. Dimitriadis, P. Maragos, and A. Potamianos, in Proc. Eurospeech'05, Sep. 2005), a novel feature set called "Teager energy cepstrum coefficients" (TECCs) has been proposed, employing a dense, smooth filterbank and alternative energy computation schemes. TECCs were shown to be more robust to noise and exhibit improved performance compared to the widely used Mel frequency cepstral coefficients (MFCCs). In this paper, we attempt to interpret these results using a combined theoretical and experimental analysis framework. Specifically, we investigate in detail the connection between the filterbank design, i.e., the filter shape and bandwidth, the energy estimation scheme and the automatic speech recognition (ASR) performance under a variety of additive and/or convolutional noise conditions. For this purpose: 1) the performance of filterbanks using triangular, Gabor, and Gammatone filters with various bandwidths and filter positions are examined under different noisy speech recognition tasks, and 2) the squared amplitude and Teager-Kaiser energy operators are compared as two alternative approaches of computing the signal energy. Our end-goal is to understand how to select the most efficient filterbank and energy computation scheme that are maximally robust under both clean and noisy recording conditions. Theoretical and experimental results show that: 1) the filter bandwidth is one of the most important factors affecting speech recognition performance in noise, while the shape of the filter is of secondary importance, and 2) the Teager-Kaiser operator outperforms (on the average and for most noise types) the squared amplitude energy computation scheme for speech recognition in noisy conditions, especially, for large filter bandwidths. Experimental results show that selecting the appropriate filterbank and energy computation scheme can lead to significant error rate reduction over both MFCC and perceptual linear predicion (PLP) features for a variety of speech recognition tasks. A relative error rate reduction of up to 30% for MFCCs and 39% for PLPs is shown for the Aurora-3 Spanish Task.

The aim of this paper is to find a multiple window estimator that is mean square error optimal for cepstrum estimation. The estimator is compared with some known multiple window methods as well as with the parametric AR-estimator. The results show that the new estimator has high performance, especially for data with large spectral dynamics, and that it is also robust against parameter choices. Simulated speech data is used for the evaluation. It is also shown that the windows of the estimator can be approximated with the sinusoidal multiple windows and that the weighting factors of the different periodograms can be analytically computed.

Este trabalho apresenta um estudo comparativo entre diferentes famílias de Transformada Wavelet aplicadas à análise cepstral de sinais digitais de fala humana, com o objetivo específico de determinar o período de pitch dos mesmos e, ao final, propõe um algoritmo diferencial para realizar tal operação, levando-se em consideração aspectos importantes do ponto de vista computacional, tais como: desempenho, complexidade do algoritmo, plataforma utilizada, dentre outros. São apresentados também, os resultados obtidos através da implementação da nova técnica (baseada na transformada wavelet) em comparação com a abordagem tradicional (baseada na transformada de Fourier). A implementação da técnica foi testada em linguagem C++ padrão ANSI sob as plataformas Windows XP Professional SP3, Windows Vista Business SP1, Mac OSX Leopard e Linux Mandriva 10.

Monitoring the condition of rolling element bearing and diagnosing their faults are cumbrous jobs. Fortunately, we have machines to do the burdensome task for us. The contemporary development in the field of machine learning allows us not only to extract features from fault signals accurately but to analyze them and predict future bearing faults almost accurately as well in a systematic manner. Utilizing an ensemble learning method named Gradient Boosting (GB) our paper proposes a technique to previse future fault classes based on the data obtained from analyzing the recorded fault data. To demonstrate the cogency of the method, we applied it on the REB fault data provided by the Case Western Reserve University (CWRU) Lab. Employing this supervised learning algorithm after preprocessing the fault signals using real cepstrum analysis, we can detect and prefigure different types of bearing faults with a staggering 99.58% accuracy.

Rolling bearings are one of the pivotal mechanical elements in rotating machines like the electric motor. However, they are liable for the majority of the faults encountered by rotating machines. Detection or estimation of these faults at an early stage can help to eliminate them and prevent the machine from malfunctioning or failing during operation. The recent developments in the field of Machine Learning (ML) have brought a radical change in the way we interpret and analyze these faults, and certain learning techniques have enabled us to predict motor bearing faults almost impeccably. This paper proposes a method to diagnose bearing fault signals that employ an ensemble learning method named Random Forest (RF). The procedure associated with this method requires simple preprocessing using Fast Fourier Transform (FFT) that explore bearing vibration signal to reveal intrinsic features about fault which are used with RF for classifying fault types. The potency of the proposed method is demonstrated using the practical motor vibration data obtained from the Case Western Reserve University (CWRU) Lab. This supervised learning algorithm is able to classify and predict various types of bearing faults with almost 99% accuracy.