Noise

About: Noise is a research topic. Over the lifetime, 110441 publications have been published within this topic receiving 1309581 citations. The topic is also known as: Мопсы танцуют под радио бандитов из сталкера 10 часов.

Feature extraction combining spectral noise reduction and cepstral histogram equalization for robust ASR.

Abstract: This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate the remaining non-linear effects associated to channel distortion and additive noise. The estimation of the noise spectrum for the spectral subtraction method relies on a new algorithm for speech / non-speech detection (SND) based on order statistics. This SND classification is also used for dropping long speech pauses. Results on Aurora 2 and Aurora 3 are reported.

Study on the effects of rotor-step skewing on the vibration and noise of a PMSM for electric vehicles

Abstract: Radial force F r is the main cause of electromagnetic vibration and noise in motors. In this study, the vibration and noise characteristics of a permanent magnet synchronous motor (PMSM) with consideration of rotor-step skewing are investigated. Firstly, the analytical model of F r considering rotor-step skewing is deduced based on the Maxwell stress tensor method. The influence of rotor-step skewing on F r of the PMSM is then analysed and summarised. Taking a 65 kW PMSM commonly used in electric vehicles as an example, combined with electromagnetic finite element analysis, F r is then evaluated. Secondly, an electromagnetic vibration and noise simulation analysis is conducted based on a multi-physical field joint simulation platform, from which the influence of rotor-step skewing on the vibration and noise is then summarised. Finally, a noise experiment is then carried out. The results obtained from the multi-physical analysis and the tests conducted are presented to validate the precision of the simulation models, and the accuracy of the analytical model of F r .

Adaptive Detection of Signals in Impulsive Noise Environments

Abstract: A detector structure and an adaptive algorithm are proposed for the reception of signals in noise backgrounds possessing broad-tailed probability distributions typical of impulsive noise. The adaptive detector combines the best features of linear matched filtering and hard-limiting receiver structures resulting in a small-signal SNR performance which is an improvement over either of these detectors alone. Furthermore, the adaptive detector is relatively easy to implement and is shown to provide efficient and robust performance for a wide range of underlying noise distributions.

Method of noise estimation using incremental bayesian learning

Abstract: A method and apparatus estimate additive noise in a noisy signal using incremental Bayes learning, where a time-varying noise prior distribution is assumed and hyperparameters (mean and variance) are updated recursively using an approximation for posterior computed at the preceding time step. The additive noise in time domain is represented in the log-spectrum or cepstrum domain before applying incremental Bayes learning. The results of both the mean and variance estimates for the noise for each of separate frames are used to perform speech feature enhancement in the same log-spectrum or cepstrum domain.