Empirical time series often contain observational noise. We investigate the effect of this noise on the estimated parameters of models fitted to the data. For data of physiological tremor, i.e. a small amplitude oscillation of the outstretched hand of healthy subjects, we compare the results for a linear model that explicitly includes additional observational noise to one that ignores this nois...

This paper addresses the problem of state duration modeling in combination with spectral subtraction and Rasta filtering to cancel both additive and convolutional noise in a text-dependent speaker verification task. The results presented in this paper suggest that temporal constraints can lead to reductions of 30 and 14% in the error rates at SNR equal to 0 and 6dB, respectively, without noise ...

This paper describes a robust speech detection algorithm for speech-activated hands-free applications. The system consists of three techniques: (1) noise suppression with efficient implementation, (2) robust endpoint detection and (3) speech verification using garbage modeling and confidence measure. With efficient implementation, noise suppression improves the SNR by roughly 10-20 dB. The endp...

In this work we develop two statistical estimation methods of maximum a posterior probability (MAP) and cumulative distribution function equalization (CDFE) for the speech spectral component estimation approaches with the application in the noise suppression filters. In contrast to the histogram equalization approach, the CDFE is developed here based on speech and noise spectral modeling, which...

Sinusoidal modeling has enjoyed a rich history in both speech and music applications, including sound transformations, compression, denoising, and auditory scene analysis. For such applications, the underlying signal model must efficiently capture salient audio features (Goodwin 1998). In this article, we present an accurate, efficient, and flexible three-part model for audio signals consisting...

gravity acceleration data have grand pursuit for marine applications. due to environmental effects, marine gravity observations always hold a high noise level. in this paper, we propose an approach to produce marine gravity data using satellite altimetry, high-resolution geopotential models and harmonic splines. on the one hand, harmonic spline functions have great capability for local gravity ...

The performance of automatic speech recognition systems strongly decreases whenever the speech signal is disturbed by background noise. We aim to improve noise robustness focusing on all major levels of speech recognition: feature extraction, feature enhancement, and speech modeling. Different auditory modeling concepts, speech enhancement techniques, training strategies, and model architecture...

The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model assumes sparse noise, and use the L1-norm to characterize the error term. In practice, however, the noise is much more complex and it is not appropriate to simply use a certain Lp-norm for noise modeling. We propose a generative RPCA model under the Bayesian framework ...