Noise measurement

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

Constrained iterative speech enhancement with application to speech recognition

Abstract: The basis of an improved form of iterative speech enhancement for single-channel inputs is sequential maximum a posteriori estimation of the speech waveform and its all-pole parameters, followed by imposition of constraints upon the sequence of speech spectra. The approaches impose intraframe and interframe constraints on the input speech signal. Properties of the line spectral pair representation of speech allow for an efficient and direct procedure for application of many of the constraint requirements. Substantial improvement over the unconstrained method is observed in a variety of domains. Informed listener quality evaluation tests and objective speech quality measures demonstrate the technique's effectiveness for additive white Gaussian noise. A consistent terminating point of the iterative technique is shown. The current systems result in substantially improved speech quality and linear predictive coding (LPC) parameter estimation with only a minor increase in computational requirements. The algorithms are evaluated with respect to improving automatic recognition of speech in the presence of additive noise and shown to outperform other enhancement methods in this application. >

Bias Reduction for an Explicit Solution of Source Localization Using TDOA

Abstract: This paper proposes two methods to reduce the bias of the well-known algebraic explicit solution (Chan and Ho, "A simple and efficient estimator for hyperbolic location," IEEE Trans. Signal Process., vol. 42, pp. 1905-1915, Aug. 1994) for source localization using TDOA. Bias of a source location estimate is significant when the measurement noise is large and the geolocation geometry is poor. Bias also dominates performance when multiple times of independent measurements are available such as in UWB localization or in target tracking. The paper starts by deriving the bias of the source location estimate from Chan and Ho. The bias is found to be considerably larger than that of the Maximum Likelihood Estimator. Two methods, called BiasSub and BiasRed, are developed to reduce the bias. The BiasSub method subtracts the expected bias from the solution of Chan and Ho's work, where the expected bias is approximated by the theoretical bias using the estimated source location and noisy data measurements. The BiasRed method augments the equation error formulation and imposes a constraint to improve the source location estimate. The BiasSub method requires the exact knowledge of the noise covariance matrix and BiasRed only needs the structure of it. Analysis shows that both methods reduce the bias considerably and achieve the CRLB performance for distant source when the noise is Gaussian and small. The BiasSub method can nearly eliminate the bias and the BiasRed method is able to lower the bias to the same level as the Maximum Likelihood Estimator. The BiasRed method is extended for TDOA and FDOA positioning. Simulations corroborate the performance of the proposed methods.

1/f frequency noise effects on self-heterodyne linewidth measurements

Abstract: The effects of a 1/f frequency noise on self-heterodyne detection are described, and the results are applied to the problem of laser diode linewidth measurement. The self-heterodyne autocorrelation function and power spectrum are evaluated for both the white and the 1/f components of the frequency noise. From numerical analysis, the power spectrum resulting from the 1/f frequency noise is shown to be approximately Gaussian, and an empirical expression is given for its linewidth. These results are applied to the problem of self-heterodyne linewidth measurements for coherent optical communications, and the amount of broadening due to 1/f frequency noise is predicted. >

Speech reception in quiet and in noisy conditions by individuals with noise-induced hearing loss in relation to their audiogram

Abstract: Tone thresholds and speech-reception thresholds were measured in 200 individuals (400 ears) with noise-induced hearing loss. The speech-reception thresholds were measured in a quiet condition and in noise with a speech spectrum at levels of 35, 50, 65, and 80 dBA. The tone audiograms could be described by three principal components: hearing loss in the regions above 3 kHz, from 1 to 3 kHz and below 1 kHz; the speech thresholds could be described by two components: speech reception in quiet and speech reception in noise at 50-80 dBA. Hearing loss above 1 kHz was related to speech reception in noise; hearing loss at and below 1 kHz to speech reception in quiet. The correlation between the speech thresholds in quiet and in noise was only R = 0.45. An adequate predictor of the speech threshold in noise, the primary factor in the hearing handicap, was the pure-tone average at 2 and 4 kHz (PTA2,4, R = 0.72). The minimum value of the prediction error for any tone-audiometric predictor of this speech threshold was 1.2 dB (standard deviation). The prediction could not be improved by taking into account the critical ratio for low-frequency noise nor by its upward spread of masking. The prediction error is due to measurement error and to a factor common to both ears. The latter factor is ascribed to cognitive skill in speech reception. Hearing loss above 10 to 15 dB HL (hearing level) already shows an effect on the speech threshold in noise, a noticeable handicap is found at PTA2,4 = 30 dB HL.

A Review Paper: Noise Models in Digital Image Processing.

Abstract: Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.