Pre-sorting of Norway spruce structural timber using acoustic measurements combined with site-, tree- and log characteristics

Density, modulus of elasticity (MOE), and bending strength (MOR) are important properties of structural timber, and knowledge about the variability of these properties is important to make efficient use of the timber. To utilize such information in the production of structural timber, the information must be available before sawing. This study presents models describing the variability of density and bending properties of Norway spruce (Picea abies L. Karst) boards within individual trees, as well as among trees and stands, based on geographical data and forest inventory data including external tree measurements. The models were based on 1551 boards from 17 sites in Southern Norway, Eastern Norway, and Trøndelag. Important variables describing variation in density, MOE, and MOR between sites were site index and elevation. For density, latitude gave additional information. Age, diameter at breast height, and longitudinal position within the tree were the most important variables at the tree level. The models explained major parts of the site variance of all properties, and for MOR, they explained a substantial part of the variance due to trees. In addition to being used for predicting the properties of structural timber from current forest resources, the models also provide information that can be used to predict the effects of silviculture on timber properties in future forest stands.

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Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-20-00212 ◽

2020 ◽

Vol 63 (12) ◽

pp. 3991-3999

Author(s):

Benjamin van der Woerd ◽

Min Wu ◽

Vijay Parsa ◽

Philip C. Doyle ◽

Kevin Fung

Keyword(s):

Repeated Measures ◽

Voice Quality ◽

Data Sets ◽

Acoustic Measurements ◽

Sample Collection ◽

Experimental Conditions ◽

Environment Analysis ◽

Acoustic Measures ◽

Recording Conditions ◽

Cepstral Peak Prominence

Objectives This study aimed to evaluate the fidelity and accuracy of a smartphone microphone and recording environment on acoustic measurements of voice. Method A prospective cohort proof-of-concept study. Two sets of prerecorded samples (a) sustained vowels (/a/) and (b) Rainbow Passage sentence were played for recording via the internal iPhone microphone and the Blue Yeti USB microphone in two recording environments: a sound-treated booth and quiet office setting. Recordings were presented using a calibrated mannequin speaker with a fixed signal intensity (69 dBA), at a fixed distance (15 in.). Each set of recordings (iPhone—audio booth, Blue Yeti—audio booth, iPhone—office, and Blue Yeti—office), was time-windowed to ensure the same signal was evaluated for each condition. Acoustic measures of voice including fundamental frequency ( f o ), jitter, shimmer, harmonic-to-noise ratio (HNR), and cepstral peak prominence (CPP), were generated using a widely used analysis program (Praat Version 6.0.50). The data gathered were compared using a repeated measures analysis of variance. Two separate data sets were used. The set of vowel samples included both pathologic ( n = 10) and normal ( n = 10), male ( n = 5) and female ( n = 15) speakers. The set of sentence stimuli ranged in perceived voice quality from normal to severely disordered with an equal number of male ( n = 12) and female ( n = 12) speakers evaluated. Results The vowel analyses indicated that the jitter, shimmer, HNR, and CPP were significantly different based on microphone choice and shimmer, HNR, and CPP were significantly different based on the recording environment. Analysis of sentences revealed a statistically significant impact of recording environment and microphone type on HNR and CPP. While statistically significant, the differences across the experimental conditions for a subset of the acoustic measures (viz., jitter and CPP) have shown differences that fell within their respective normative ranges. Conclusions Both microphone and recording setting resulted in significant differences across several acoustic measurements. However, a subset of the acoustic measures that were statistically significant across the recording conditions showed small overall differences that are unlikely to have clinical significance in interpretation. For these acoustic measures, the present data suggest that, although a sound-treated setting is ideal for voice sample collection, a smartphone microphone can capture acceptable recordings for acoustic signal analysis.

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