(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Digital processing of speech signals

The health impacts of environmental noise are a growing concern. At least one million healthy life years are lost every year from traffic-related noise in the western part of Europe. This publication summarises the evidence on the relationship between environmental noise and health effects, including cardiovascular disease, cognitive impairment, sleep disturbance, tinnitus, and annoyance. For each one, the environmental burden of disease methodology, based on exposure-response relationship, exposure distribution, background prevalence of disease and disability weights of the outcome, is applied to calculate the burden of disease in terms of disability-adjusted life-years. Data are still lacking for the rest of the WHO European Region. This publication provides policy-makers and their advisers with technical support in their quantitative risk assessment of environmental noise. International, national and local authorities can use the procedure for estimating burdens presented here to prioritize and plan environmental and public health policies.

Burden of Disease from Environmental Noise: Quantification of Healthy Life Years Lost in Europe

Working memory is important for online language processing during conversation. We use it to maintain relevant information, to inhibit or ignore irrelevant information, and to attend to conversation selectively. Working memory helps us to keep track of and actively participate in conversation, including taking turns and following the gist. This paper examines the Ease of Language Understanding model (i.e., the ELU model, Ronnberg, 2003; Ronnberg et al., 2008) in light of new behavioral and neural findings concerning the role of working memory capacity (WMC) in uni-modal and bimodal language processing. The new ELU model is a meaning prediction system that depends on phonological and semantic interactions in rapid implicit and slower explicit processing mechanisms that both depend on WMC albeit in different ways. It is based on findings that address the relationship between WMC and (a) early attention processes in listening to speech, (b) signal processing in hearing aids and its effects on short-term memory, (c) inhibition of speech maskers and its effect on episodic long-term memory, (d) the effects of hearing impairment on episodic and semantic long-term memory, and finally, (e) listening effort. New predictions and clinical implications are outlined. Comparisons with other WMC and speech perception models are made.

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The Ease of Language Understanding (ELU) model: Theoretical, empirical, and clinical advances

The Journal of the Acoustical Society of America

Background:Exposure to noise is highly prevalent in the workplace, and an etiologic association with cardiovascular disease has been hypothesized. Although there is evidence of hypertension among noise-exposed workers, evidence of heart disease has been less conclusive.Methods:We identified a cohort

https://www.jstor.org/stable/pdfplus/20485996.pdf

Occupational Exposure to Noise and Mortality From Acute Myocardial Infarction

A method has been developed for determining typical long-term speech and background-noise levels during lectures. Lectures are recorded and the recordings digitized and processed to obtain sound-pressure-level frequency distributions to which three normal-distribution curves are fit. The maximum values of these curves are associated with long-term sound-pressure levels associated with speech, ventilation noise, and student-activity noise. Recordings made during 18 university lectures in 11 classrooms have been analyzed. Average (standard deviation) A-weighted levels for the various sound components were determined as follows: ventilation noise, 40.9 (3.9) dB; student-activity noise, 41.9 (4.0) dB; total background noise, 44.4 (3.5) dB; and received speech-signal, 50.8 (3.9) dB. The average (standard deviation) A-weighted speech-signal to background-noise ratio was 7.9 (3.1) dB. That of the instructor sound-power level was 64.5 (4.2) dB. Empirical models have been developed to predict the room-average A-weighted results using multivariable regression analysis. Further analysis in the 63- to 8000-Hz octave bands confirmed the spectra of ventilation noise and of speech, and determined the spectrum of student-activity noise and of the speech-signal to background-noise ratio.

Measurement and prediction of typical speech and background-noise levels in university classrooms during lectures

The question of what is the optimal reverberation time for speech intelligibility in an occupied classroom has been studied recently in two different ways, with contradictory results. Experiments have been performed under various conditions of speech-signal to background-noise level difference and reverberation time, finding an optimal reverberation time of zero. Theoretical predictions of appropriate speech-intelligibility metrics, based on diffuse-field theory, found nonzero optimal reverberation times. These two contradictory results are explained by the different ways in which the two methods account for background noise, both of which are unrealistic. To obtain more realistic and accurate predictions, noise sources inside the classroom are considered. A more realistic treatment of noise is incorporated into diffuse-field theory by considering both speech and noise sources and the effects of reverberation on their steady-state levels. The model shows that the optimal reverberation time is zero when the speech source is closer to the listener than the noise source, and nonzero when the noise source is closer than the speech source. Diffuse-field theory is used to determine optimal reverberation times in unoccupied classrooms given optimal values for the occupied classroom. Resulting times can be as high as several seconds in large classrooms; in some cases, optimal values are unachievable, because the occupants contribute too much absorption.

Effect of noise and occupancy on optimal reverberation times for speech intelligibility in classrooms.

In a recent publication [M. Hodgson, J. Acoust. Soc. Am. 84, 253–261 (1988)], predictions were compared with measurements made in an empty scale‐model room and in various nominally empty factories and gymnasia. Consistent deviations between prediction and experiment were found. In order to explain these deviations, it was hypothesized that diffuse surface reflections were occurring in the test rooms. To test this hypothesis, further predictions have now been made using a ray‐tracing model that takes diffuse surface reflection into account. It was found that excellent agreement between prediction and experiment is obtained as follows: (a) in the scale model, if all surfaces are 10%–40% diffusely reflecting; (b) in nominally empty factories, if the ceiling and walls are 60%–90% diffusely reflecting. This result represents strong evidence that typical untreated room surfaces are strongly diffusely reflecting.

Evidence of diffuse surface reflections in rooms

Acoustical measurements were performed in 30 randomly chosen, unoccupied classrooms at the University of British Columbia (UBC). Tests had previously been done in 46 unoccupied UBC classrooms, as well as in 10 of these when occupied by students. The results for the 10 classrooms were used to correct the “unoccupied” results to the half-occupied and fully occupied conditions. The objective of the work was to characterize the 30 classrooms, which were used in subsequent studies, to determine the acoustical quality of the UBC classroom stock and how this depends on the classroom design and the presence of students, and to elucidate characteristics of classroom acoustics relevant to optimal design. The results showed that the UBC classroom stock is of far from optimum acoustical quality when unoccupied, but is much better in the occupied condition. Generally, many classrooms have excessive reverberation and result in low speech levels, especially at the back of the rooms; in addition, they have excessively noisy ventilation systems.

Murray Hodgson

Papers

Occupational Exposure to Noise and Mortality From Acute Myocardial Infarction

Measurement and prediction of typical speech and background-noise levels in university classrooms during lectures

Effect of noise and occupancy on optimal reverberation times for speech intelligibility in classrooms.

Evidence of diffuse surface reflections in rooms

Experimental investigation of the acoustical characteristics of university classrooms