Perceptual compensation for reverberation in speech identification: effects of single-band, multiple-band and wideband noise contexts

Perceptual effects of room reverberation on a "sir" or "stir" test-word can be observed when the level of reverberation in the word is increased, while the reverberation in a surrounding 'context I utterance remains at a minimal level. The result is that listeners make more "sit" identifications. When the context's reverberation is also increased, to approach the level in the test word, extrinsic perceptual compensation is observed, so that the number of listeners' "sir" identifications reduces to a value similar to that found with minimal reverberation. Thus far, compensation effects have only been observed with speech or speech-like contexts in which the short-term spectrum changes as the speaker's articulators move. The results reported here show that some noise contexts with static short-term spectra can also give rise to compensation. From these experiments it would appear that compensation requires a context with a temporal envelope that fluctuates to some extent, so that parts of it resemble offsets. These findings are consistent with a rather general kind of perceptual compensation mechanism; one that is informed by the 'tails' that reverberation adds at offsets. Other results reported here show that narrow-band contexts do not bring about compensation, even when their temporal-envelopes are the same as those of the more effective wideband contexts. These results suggest that compensation is confined to the frequency range occupied by the context, and that in a wideband sound it might operate in a 'band by band' manner.

Auditory distraction from low-intensity noise: a review of the consequences for learning and workplace environments

The 'irrelevant sound effect' in short-term memory is commonly believed to entail a number of direct consequences for cognitive performance in the office and other workplaces (e.g. S. P. Banbury, S. Tremblay, W. J. Macken, & D. M. Jones, 2001). It may also help to identify what types of sound are most suitable as auditory warning signals. However, the conclusions drawn are based primarily upon evidence from a single task (serial recall) and a single population (young adults). This evidence is reconsidered from the standpoint of different worker populations confronted with common workplace tasks and auditory environments. Recommendations are put forward for factors to be considered when assessing the impact of auditory distraction in the workplace. Copyright (c) 2005 John Wiley & Sons, Ltd.

Office noise and employee concentration: identifying causes of disruption and potential improvements

A field study assessed subjective reports of distraction from various office sounds among 88 employees at two sites. In addition, the study examined the amount of exposure the workers had to the noise in order to determine any evidence for habituation. Finally, respondents were asked how they would improve their environment ( with respect to noise), and to rate examples of improvements with regards to their job satisfaction and performance. Out of the sample, 99% reported that their concentration was impaired by various components of office noise, especially telephones left ringing at vacant desks and people talking in the background. No evidence for habituation to these sounds was found. These results are interpreted in the light of previous research regarding the effects of noise in offices and the 'irrelevant sound effect'.

A new algorithm for OFDM joint data detection and phase noise cancellation

This paper proposes a new iterative algorithm for OFDM joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the problem of "overfitting" such that the iterative approach may converge to a trivial solution. Although it is essential for this joint approach, the overfitting problem was relatively less studied in existing algorithms. In this paper, specifically, we apply a hard decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the phase noise, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical simulations are also given to verify the proposed algorithm.

Robust classification of SAR imagery

In this work the G(A)(0) distribution is assumed as the universal model for amplitude Synthetic Aperture (SAR) imagery data under the Multiplicative Model. The observed data, therefore, is assumed to obey a G(A)(0) (alpha; gamma, n) law, where the parameter n is related to the speckle noise, and (alpha, gamma) are related to the ground truth, giving information about the background. Therefore, maps generated by the estimation of (alpha, gamma) in each coordinate can be used as the input for classification methods. Maximum likelihood estimators are derived and used to form estimated parameter maps. This estimation can be hampered by the presence of corner reflectors, man-made objects used to calibrate SAR images that produce large return values. In order to alleviate this contamination, robust (M) estimators are also derived for the universal model. Gaussian Maximum Likelihood classification is used to obtain maps using hard-to-deal-with simulated data, and the superiority of robust estimation is quantitatively assessed.

Design, implementation and evaluation of broadband low noise amplifier (LNA) for radiometer

The two major applications of microwave remote sensors are radiometer and radar. Because of its importance and the nature of the application, much research has been made on the various aspects of the radar. This paper will focus on the various aspects of the radiometer from a design point of view and the Low Noise Amplifier will be designed and implemented. The paper is based on a study in radio Frequency Communications engineering and understanding of electronic and RF circuits. Some research study about the radiometer and practical implementation of Low Noise Amplifier for Radiometer will be the main focus of this paper. Basically the paper is divided into two parts. In the first part some background study about the radiometer will be carried out and commonly used types of radiometer will be discussed. In the second part LNA for the radiometer will be designed.

OFDM joint data detection and phase noise cancellation based on minimum mean square prediction error

This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.

OFDM joint data detection and phase noise cancellation for constant modulus modulations

This correspondence proposes a new algorithm for the OFDM joint data detection and phase noise (PHN) cancellation for constant modulus modulations. We highlight that it is important to address the overfitting problem since this is a major detrimental factor impairing the joint detection process. In order to attack the overfitting problem we propose an iterative approach based on minimum mean square prediction error (MMSPE) subject to the constraint that the estimated data symbols have constant power. The proposed constrained MMSPE algorithm (C-MMSPE) significantly improves the performance of existing approaches with little extra complexity being imposed. Simulation results are also given to verify the proposed algorithm.

High signal to noise ratio THz spectroscopy with ASOPS and signal processing schemes for mapping and controlling molecular and bulk relaxation processes

Asynchronous Optical Sampling has the potential to improve signal to noise ratio in THz transient sperctrometry. The design of an inexpensive control scheme for synchronising two femtosecond pulse frequency comb generators at an offset frequency of 20 kHz is discussed. The suitability of a range of signal processing schemes adopted from the Systems Identification and Control Theory community for further processing recorded THz transients in the time and frequency domain are outlined. Finally, possibilities for femtosecond pulse shaping using genetic algorithms are mentioned.

Green noise or green value? Measuring the effects of environmental certification on office values

This study investigates the price effects of environmental certification on commercial real estate assets. It is argued that there are likely to be three main drivers of price differences between certified and noncertified buildings. These are additional occupier benefits, lower holding costs for investors and a lower risk premium. Drawing upon the CoStar database of U.S. commercial real estate assets, hedonic regression analysis is used to measure the effect of certification on both rent and price. The results suggest that, compared to buildings in the same submarkets, eco-certified buildings have both a rental and sale price premium.

Modified level set method with Canny operator for image noise removal

The level set method is commonly used to address image noise removal. Existing studies concentrate mainly on determining the speed function of the evolution equation. Based on the idea of a Canny operator, this letter introduces a new method of controlling the level set evolution, in which the edge strength is taken into account in choosing curvature flows for the speed function and the normal to edge direction is used to orient the diffusion of the moving interface. The addition of an energy term to penalize the irregularity allows for better preservation of local edge information. In contrast with previous Canny-based level set methods that usually adopt a two-stage framework, the proposed algorithm can execute all the above operations in one process during noise removal.

Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown non-linear processes

This paper presents a controller design scheme for a priori unknown non-linear dynamical processes that are identified via an operating point neurofuzzy system from process data. Based on a neurofuzzy design and model construction algorithm (NeuDec) for a non-linear dynamical process, a neurofuzzy state-space model of controllable form is initially constructed. The control scheme based on closed-loop pole assignment is then utilized to ensure the time invariance and linearization of the state equations so that the system stability can be guaranteed under some mild assumptions, even in the presence of modelling error. The proposed approach requires a known state vector for the application of pole assignment state feedback. For this purpose, a generalized Kalman filtering algorithm with coloured noise is developed on the basis of the neurofuzzy state-space model to obtain an optimal state vector estimation. The derived controller is applied in typical output tracking problems by minimizing the tracking error. Simulation examples are included to demonstrate the operation and effectiveness of the new approach.