Improvement in the shallow water prediction of sonobuoy detection ranges for ASW aircraft using on‐site acoustic measurements

1994 ◽  
Vol 95 (5) ◽  
pp. 2884-2884
Author(s):  
Charles Wiseman
Keyword(s):  
Shallow Water ◽  
Acoustic Measurements

Seabed sound speed and attenuation from broadband acoustic measurements in the Shallow Water 2006 experiment.

2009 ◽  
Vol 126 (4) ◽  
pp. 2193
Author(s):  
Lin Wan ◽  
Ji-Xun Zhou ◽  
Peter H. Rogers ◽  
David P. Knobles
Keyword(s):  
Shallow Water ◽  
Sound Speed ◽  
Acoustic Measurements

Recent shallow‐water acoustic measurements of sound transmission, reverberation, and coherence.

1996 ◽  
Vol 99 (4) ◽  
pp. 2523-2529 ◽  
Author(s):  
William M. Carey ◽  
Peter Cable ◽  
Mike Steele
Keyword(s):  
Shallow Water ◽  
Sound Transmission ◽  
Acoustic Measurements

Shallow‐water acoustic measurements in the southern Adriatic Sea

1995 ◽  
Vol 98 (5) ◽  
pp. 2953-2953
Author(s):  
J. M. Berkson ◽  
R. D. Hollett ◽  
M. Max
Keyword(s):  
Shallow Water ◽  
Adriatic Sea ◽  
Acoustic Measurements

Acoustic measurements in shallow water using an ocean glider

2007 ◽  
Vol 121 (5) ◽  
pp. 3108-3108 ◽  
Author(s):  
Georges A. Dossot ◽  
James H. Miller ◽  
Gopu R. Potty ◽  
Kristy A. Moore ◽  
Jason D. Holmes ◽  
...  
Keyword(s):  
Shallow Water ◽  
Acoustic Measurements

Evaluation of Acoustic Analyses of Voice in Nonoptimized Conditions

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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