Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Supply Voltage Noise Aware ATPG for Transition Delay Faults

Abstract: The sensitivity of very deep submicron designs to supply voltage noise is increasing due to higher path delay variations and reduced noise margins with supply noise scaling. The supply noise of delay test during at-speed launch and capture is significantly larger compared to normal circuit operation since larger number of transitions occur within a short time frame. Our simulations have shown that for identical switching activity, a pattern with a short switching time frame window will surge more current from the power network, thereby causing higher IR-drop. In this paper, the authors propose a novel method to measure the average power of at-speed test patterns, referred to as switching cycle average power (SCAP). The authors present a case study of the IR-drop effects on design performance during at-speed test. A new practical framework is proposed to generate supply noise tolerant delay test patterns. The proposed framework uses existing commercial ATPG tools and a wrapper is added around them. The results demonstrate that the new patterns generated using our framework will significantly reduce the supply noise

Environment-Optimized Speech Enhancement

Abstract: In this paper, we present a training-based approach to speech enhancement that exploits the spectral statistical characteristics of clean speech and noise in a specific environment. In contrast to many state-of-the-art approaches, we do not model the probability density function (pdf) of the clean speech and the noise spectra. Instead, subband-individual weighting rules for noisy speech spectral amplitudes are separately trained for speech presence and speech absence from noise recordings in the environment of interest. Weighting rules for a variety of cost functions are given; they are parameterized and stored as a table look-up. The speech enhancement system simply works by computing the weighting rules from the table look-up indexed by the a posteriori signal-to-noise ratio (SNR) and the a priori SNR for each subband computed on a Bark scale. Optimized for an automotive environment, our approach outperforms known-environment-independent-speech enhancement techniques, namely the a priori SNR-driven Wiener filter and the minimum mean square error (MMSE) log-spectral amplitude estimator, both in terms of speech distortion and noise attenuation.

Transient noise characterization and filtration in CCD cameras exposed to stray radiation from a medical linear accelerator.

Abstract: Charge coupled devices (CCDs) are being increasingly used in radiation therapy for dosimetric purposes. However, CCDs are sensitive to stray radiation. This effect induces transient noise. Radiation-induced noise strongly alters the image and therefore limits its quantitative analysis. The purpose of this work is to characterize the radiation-induced noise and to develop filtration algorithms to restore image quality. Two models of CCD were used for measurements close to a medical linac. The structure of the transient noise was first characterized. Then, four methods of noise filtration were compared: median filtering of a time series of identical images, uniform median filtering of single images, an adaptive filter with switching mechanism, and a modified version of the adaptive switch filter. The intensity distribution of noisy pixels was similar in both cameras. However, the spatial distribution of the noise was different: The average noise cluster size was 1.2 +/- 0.6 and 3.2 +/- 2.7 pixels for the U2000 and the Luca, respectively. The median of a time series of images resulted in the best filtration and minimal image distortion. For applications where time series is impractical, the adaptive switch filter must be used to reduce image distortion. Our modified version of the switch filter can be used in order to handle nonisolated groups of noisy pixels.

Noise reduction by paired-microphones using spectral subtraction

Abstract: This paper proposes a method of noise reduction by paired microphones as a front-end processor for speech recognition systems. This method estimates noise using a subtractive microphone array and subtracts them from the noisy speech signal using spectral subtraction (SS). Since this method can estimate noise analytically and frame by frame, it is easy to estimate noise not depending on these acoustic properties. Therefore, this method can also reduce non-stationary noise, for example sudden noise when a door has just closed, which cannot be reduced by other SS methods. The results of computer simulations and experiments in a real environment show that this method can reduce LPC log spectral envelope distortions.