Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

A Markov-Middleton Model for Bursty Impulsive Noise: Modeling and Receiver Design

Abstract: Transmission over channels impaired by impulsive noise, such as in power substations, calls for peculiar mitigation techniques at the receiver side in order to cope with signal deterioration. For these techniques to be effective, a reliable noise model is usually required. One of the widely accepted models is the Middleton Class A, which presents the twofold advantage to be canonical (i.e., invariant of the particular physical source mechanisms) and to exhibit a simple probability density function (PDF) that only depends on three physical parameters, making this model very attractive. However, such a model fails in replicating bursty impulsive noise, where each impulse spans over several consecutive noise samples, as usually observed (e.g., in power substations). Indeed, the Middleton Class A model only deals with amplitude or envelope statistics. On the other hand, for models based on Markov chains, although they reproduce the bursty nature of impulses, the determination of the suitable number of states and the noise distribution associated with each state can be challenging. In this paper, 1) we introduce a new impulsive noise model which is, in fact, a Hidden Markov Model, whose realizations exactly follow a Middleton Class A distribution and 2) we evaluate optimum and suboptimum detections for a coded transmission impaired by the proposed noise model.

Separation and Determination of the Disturbing Signals in Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR)

Abstract: Phase-sensitive optical time domain reflectometry (Φ-OTDR) is easy to be interfered by ambient noises, and the nonlinear coherent addition of different interferences always makes it difficult to detect real human intrusions and causes high nuisance alarm rates (NARs) in practical applications. In this paper, an effective temporal signal separation and determination method is proposed to improve its detection performance in complicated noisy environments. Unlike the conventional analysis of transverse spatial signals, the time-evolving sensing signal of Φ-OTDR system is at first obtained for each spatial point by accumulating the changing OTDR traces at different moments. Then, its longitudinal temporal signal is decomposed and analyzed by a multi-scale wavelet decomposition method. By selectively recombining the corresponding scale components, it can effectively extract human intrusion signals, and separate the influences of slow change of the system and other environmental interferences. Compared with the conventional differentiation way and fast Fourier transformation denoising method, the SNRs of the detecting signals for the proposed method is always the best, which can be raised by up to ∼35 dB for the best case. Moreover, from the decomposed components, different event signals can be effectively determined by their energy distribution features, and the NAR can be controlled to be less than 2% in the test.

Towards noise-robust speaker recognition using probabilistic linear discriminant analysis

Abstract: This work addresses the problem of speaker verification where additive noise is present in the enrollment and testing utterances. We show how the current state-of-the-art framework can be effectively used to mitigate this effect. We first look at the degradation a standard speaker verification system is subjected to when presented with noisy speech waveforms. We designed and generated a corpus with noisy conditions, based on the NIST SRE 2008 and 2010 data, built using open-source tools and freely available noise samples. We then show how adding noisy training data in the current i-vector-based approach followed by probabilistic linear discriminant analysis (PLDA) can bring significant gains in accuracy at various signal-to-noise ratio (SNR) levels. We demonstrate that this improvement is not feature-specific as we present positive results for three disparate sets of features: standard mel frequency cepstral coefficients, prosodic polynomial co-efficients and maximum likelihood linear regression (MLLR) transforms.