The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

David A Mann - One of the best experts on this subject based on the ideXlab platform.

  • underwater hearing in the loggerhead turtle caretta caretta a comparison of behavioral and auditory evoked potential Audiograms
    The Journal of Experimental Biology, 2012
    Co-Authors: Joseph C Gaspard, Gordon B. Bauer, David A Mann, Kelly J Martin, Sarah C Alessi, Anton D Tucker
    Abstract:

    SUMMARY The purpose of this study was to compare underwater behavioral and auditory evoked potential (AEP) Audiograms in a single captive adult loggerhead sea turtle ( Caretta caretta ). The behavioral Audiogram was collected using a go/no-go response procedure and a modified staircase method of threshold determination. AEP thresholds were measured using subdermal electrodes placed beneath the frontoparietal scale, dorsal to the midbrain. Both methods showed the loggerhead sea turtle to have low frequency hearing with best sensitivity between 100 and 400 Hz. AEP testing yielded thresholds from 100 to 1131 Hz with best sensitivity at 200 and 400 Hz (110 dB re. 1 μPa). Behavioral testing using 2 s tonal stimuli yielded underwater thresholds from 50 to 800 Hz with best sensitivity at 100 Hz (98 dB re. 1 μPa). Behavioral thresholds averaged 8 dB lower than AEP thresholds from 100 to 400 Hz and 5 dB higher at 800 Hz. The results suggest that AEP testing can be a good alternative to measuring a behavioral Audiogram with wild or untrained marine turtles and when time is a crucial factor.

  • research article Audiogram and auditory critical ratios of two florida manatees trichechus manatus latirostris
    2012
    Co-Authors: Joseph C Gaspard, Kimberly Dziuk, Adrienne Cardwell, Gordon B. Bauer, Roger L. Reep, David A Mann
    Abstract:

    SUMMARY Manatees inhabit turbid, shallow-water environments and have been shown to have poor visual acuity. Previous studies on hearing have demonstrated that manatees possess good hearing and sound localization abilities. The goals of this research were to determine the hearing abilities of two captive subjects and measure critical ratios to understand the capacity of manatees to detect tonal signals, such as manatee vocalizations, in the presence of noise. This study was also undertaken to better understand individual variability, which has been encountered during behavioral research with manatees. Two Florida manatees (Trichechus manatus latirostris) were tested in a go/no-go paradigm using a modified staircase method, with incorporated ʻcatchʼ trials at a 1:1 ratio, to assess their ability to detect single-frequency tonal stimuli. The behavioral Audiograms indicated that the manateesʼ auditory frequency detection for tonal stimuli ranged from 0.25 to 90.5kHz, with peak sensitivity extending from 8 to 32kHz. Critical ratios, thresholds for tone detection in the presence of background masking noise, were determined with oneoctave wide noise bands, 7–12dB (spectrum level) above the thresholds determined for the Audiogram under quiet conditions. Manatees appear to have quite low critical ratios, especially at 8kHz, where the ratio was 18.3dB for one manatee. This suggests that manatee hearing is sensitive in the presence of background noise and that they may have relatively narrow filters in the tested frequency range.

Dennis L. Barbour - One of the best experts on this subject based on the ideXlab platform.

  • Fast, Continuous Audiogram Estimation Using Machine Learning
    Ear and Hearing, 2015
    Co-Authors: Xinyu D. Song, Jacob R Gardner, Brittany M. Wallace, Nicholus M. Ledbetter, Kilian Q. Weinberger, Dennis L. Barbour
    Abstract:

    OBJECTIVES Pure-tone audiometry has been a staple of hearing assessments for decades. Many different procedures have been proposed for measuring thresholds with pure tones by systematically manipulating intensity one frequency at a time until a discrete threshold function is determined. The authors have developed a novel nonparametric approach for estimating a continuous threshold Audiogram using Bayesian estimation and machine learning classification. The objective of this study was to assess the accuracy and reliability of this new method relative to a commonly used threshold measurement technique. DESIGN The authors performed air conduction pure-tone audiometry on 21 participants between the ages of 18 and 90 years with varying degrees of hearing ability. Two repetitions of automated machine learning Audiogram estimation and one repetition of conventional modified Hughson-Westlake ascending-descending Audiogram estimation were acquired by an audiologist. The estimated hearing thresholds of these two techniques were compared at standard Audiogram frequencies (i.e., 0.25, 0.5, 1, 2, 4, 8 kHz). RESULTS The two threshold estimate methods delivered very similar estimates at standard Audiogram frequencies. Specifically, the mean absolute difference between estimates was 4.16 ± 3.76 dB HL. The mean absolute difference between repeated measurements of the new machine learning procedure was 4.51 ± 4.45 dB HL. These values compare favorably with those of other threshold Audiogram estimation procedures. Furthermore, the machine learning method generated threshold estimates from significantly fewer samples than the modified Hughson-Westlake procedure while returning a continuous threshold estimate as a function of frequency. CONCLUSIONS The new machine learning Audiogram estimation technique produces continuous threshold Audiogram estimates accurately, reliably, and efficiently, making it a strong candidate for widespread application in clinical and research audiometry.

Darlene R. Ketten - One of the best experts on this subject based on the ideXlab platform.

  • multi species baleen whale Audiogram modelling
    Journal of the Acoustical Society of America, 2016
    Co-Authors: Darlene R. Ketten, Aleks Zosuls, Andrew A Tubelli
    Abstract:

    In this research, we produced model Audiograms for two Mysticetes (baleen whales) that are among the species most likely to be subject to impacts from common lower frequency anthropogenic sound sources deployed in the oceans. These models are needed for species-specific risk assessments for hearing impacts, for determining optimal signals for playback experiments, and for determining effective electrode and sound source placements for auditory brainstem response (ABR) measures in live stranded whales. We employed micro and UHRCT, dissection, and histology of minke (Balaenoptera acutorostrata) and humpback (Megaptera novaeangliae) heads and ears to calculate inner ear frequency maps for determining total hearing range, the frequency of peak sensitivity, and the probable frequency of NIHL liability (“notch”). The anatomically derived data were then combined with direct measures via nanoindentation of middle ear stiffness, Young’s modulus, frequency response, and inner ear stiffness to determine the middle e...

  • A comparison of auditory brainstem responses across diving bird species
    Journal of Comparative Physiology A, 2015
    Co-Authors: Sara E. Crowell, Alicia M. Wells-berlin, Catherine E. Carr, Glenn H. Olsen, Ronald E. Therrien, Sally E. Yannuzzi, Darlene R. Ketten
    Abstract:

    There is little biological data available for diving birds because many live in hard-to-study, remote habitats. Only one species of diving bird, the black-footed penguin ( Spheniscus demersus ), has been studied in respect to auditory capabilities (Wever et al., Proc Natl Acad Sci USA 63:676–680, 1969 ). We, therefore, measured in-air auditory threshold in ten species of diving birds, using the auditory brainstem response (ABR). The average Audiogram obtained for each species followed the U-shape typical of birds and many other animals. All species tested shared a common region of the greatest sensitivity, from 1000 to 3000 Hz, although Audiograms differed significantly across species. Thresholds of all duck species tested were more similar to each other than to the two non-duck species tested. The red-throated loon ( Gavia stellata ) and northern gannet ( Morus bassanus ) exhibited the highest thresholds while the lowest thresholds belonged to the duck species, specifically the lesser scaup ( Aythya affinis ) and ruddy duck ( Oxyura jamaicensis ). Vocalization parameters were also measured for each species, and showed that with the exception of the common eider ( Somateria mollisima ), the peak frequency, i.e., frequency at the greatest intensity, of all species’ vocalizations measured here fell between 1000 and 3000 Hz, matching the bandwidth of the most sensitive hearing range.

  • a prediction of the minke whale balaenoptera acutorostrata middle ear transfer function
    Journal of the Acoustical Society of America, 2012
    Co-Authors: Andrew A Tubelli, Maya Yamato, Darlene R. Ketten, Aleks Zosuls, David C Mountain
    Abstract:

    The lack of baleen whale (Cetacea Mysticeti) Audiograms impedes the assessment of the impacts of anthropogenic noise on these animals. Estimates of Audiograms, which are difficult to obtain behaviorally or electrophysiologically for baleen whales, can be made by simulating the Audiogram as a series of components representing the outer, middle, and inner ear (Rosowski, 1991; Ruggero and Temchin, 2002). The middle-ear portion of the system can be represented by the middle-ear transfer function (METF), a measure of the transmission of acoustic energy from the external ear to the cochlea. An anatomically accurate finite element model of the minke whale (Balaenoptera acutorostrata) middle ear was developed to predict the METF for a mysticete species. The elastic moduli of the auditory ossicles were measured by using nanoindentation. Other mechanical properties were estimated from experimental stiffness measurements or from published values. The METF predicted a best frequency range between approximately 30 Hz and 7.5 kHz or between 100 Hz and 25 kHz depending on stimulation location. Parametric analysis found that the most sensitive parameters are the elastic moduli of the glove finger and joints and the Rayleigh damping stiffness coefficient β. The predicted hearing range matches well with the vocalization range.

  • specialization for underwater hearing by the tympanic middle ear of the turtle trachemys scripta elegans
    Proceedings of The Royal Society B: Biological Sciences, 2012
    Co-Authors: Jakob Christensendalsgaard, Darlene R. Ketten, Christian Brandt, Katie L Willis, Christian Bech Christensen, Peggy L Eddswalton, Richard R Fay, Peter T Madsen
    Abstract:

    Turtles, like other amphibious animals, face a trade-off between terrestrial and aquatic hearing. We used laser vibrometry and auditory brainstem responses to measure their sensitivity to vibration stimuli and to airborne versus underwater sound. Turtles are most sensitive to sound underwater, and their sensitivity depends on the large middle ear, which has a compliant tympanic disc attached to the columella. Behind the disc, the middle ear is a large air-filled cavity with a volume of approximately 0.5 ml and a resonance frequency of approximately 500 Hz underwater. Laser vibrometry measurements underwater showed peak vibrations at 500–600 Hz with a maximum of 300 µm s−1 Pa−1, approximately 100 times more than the surrounding water. In air, the auditory brainstem response Audiogram showed a best sensitivity to sound of 300–500 Hz. Audiograms before and after removing the skin covering reveal that the cartilaginous tympanic disc shows unchanged sensitivity, indicating that the tympanic disc, and not the overlying skin, is the key sound receiver. If air and water thresholds are compared in terms of sound intensity, thresholds in water are approximately 20–30 dB lower than in air. Therefore, this tympanic ear is specialized for underwater hearing, most probably because sound-induced pulsations of the air in the middle ear cavity drive the tympanic disc.

  • prediction of a mysticete Audiogram via finite element analysis of the middle ear
    Advances in Experimental Medicine and Biology, 2012
    Co-Authors: Andrew A Tubelli, Darlene R. Ketten, Aleks Zosuls, David C Mountain
    Abstract:

    The impact of anthropogenic sound on marine mammals is difficult to assess, especially for species without available Audiograms. There are currently no Audiograms for any species of mysticete because of their size and, in many cases, their endangerment status. Consequently, insight into the hearing range of all mysticete species comes from indirect sources such as vocalization recordings. In contrast to mysticetes, several odontocete species have published Audiograms.

Matthew G Crowson - One of the best experts on this subject based on the ideXlab platform.

  • autoaudio deep learning for automatic Audiogram interpretation
    Journal of Medical Systems, 2020
    Co-Authors: Matthew G Crowson, Jong Wook Lee, Amr Hamour, Rafid Mahmood, Aaron Babier, Vincent Lin, Debara L Tucci, Timothy C Y Chan
    Abstract:

    Hearing loss is the leading human sensory system loss, and one of the leading causes for years lived with disability with significant effects on quality of life, social isolation, and overall health. Coupled with a forecast of increased hearing loss burden worldwide, national and international health organizations have urgently recommended that access to hearing evaluation be expanded to meet demand. The objective of this study was to develop ‘AutoAudio’ – a novel deep learning proof-of-concept model that accurately and quickly interprets diagnostic Audiograms. Adult Audiogram reports representing normal, conductive, mixed and sensorineural morphologies were used to train different neural network architectures. Image augmentation techniques were used to increase the training image set size. Classification accuracy on a separate test set was used to assess model performance. The architecture with the highest out-of-training set accuracy was ResNet-101 at 97.5%. Neural network training time varied between 2 to 7 h depending on the depth of the neural network architecture. Each neural network architecture produced misclassifications that arose from failures of the model to correctly label the Audiogram with the appropriate hearing loss type. The most commonly misclassified hearing loss type were mixed losses. Re-engineering the process of hearing testing with a machine learning innovation may help enhance access to the growing worldwide population that is expected to require audiologist services. Our results suggest that deep learning may be a transformative technology that enables automatic and accurate Audiogram interpretation.

  • autoaudio deep learning for automatic Audiogram interpretation
    medRxiv, 2020
    Co-Authors: Matthew G Crowson, Jong Wook Lee, Amr Hamour, Rafid Mahmood, Aaron Babier, Vincent Lin, Debara L Tucci, Timothy C Y Chan
    Abstract:

    Abstract Objectives Hearing loss is the leading human sensory system loss, and one of the leading causes for years lived with disability with significant effects on quality of life, social isolation, and overall health. Coupled with a forecast of increased hearing loss burden worldwide, national and international health organizations have urgently recommended that access to hearing evaluation be expanded to meet demand. Methods The objective of this study was to develop ‘AutoAudio’ – a novel deep learning proof-of-concept model that accurately and quickly interprets diagnostic Audiograms. Adult Audiogram reports representing normal, conductive, mixed and sensorineural morphologies were used to train different neural network architectures. Image augmentation techniques were used to increase the training image set size. Classification accuracy on a separate test set was used to assess model performance. Results The architecture with the highest out-of-training set accuracy was ResNet-101 at 97.5%. Neural network training time varied between 2 to 7 hours depending on the depth of the neural network architecture. Each neural network architecture produced misclassifications that arose from failures of the model to correctly label the Audiogram with the appropriate hearing loss type. The most commonly misclassified hearing loss type were mixed losses. Conclusion Re-engineering the process of hearing testing with a machine learning innovation may help enhance access to the growing worldwide population that is expected to require audiologist services. Our results suggest that deep learning may be a transformative technology that enables automatic and accurate Audiogram interpretation.

Alexander Ya Supin - One of the best experts on this subject based on the ideXlab platform.

  • binaural aep Audiograms in seven beluga whales delphinapterus leucas from the okhotsk sea population
    Journal of the Acoustical Society of America, 2018
    Co-Authors: Evgeniya Sysueva, Dmitry I Nechaev, V Popov, Mikhail B Tarakanov, Alexander Ya Supin
    Abstract:

    Hearing thresholds were measured and Audiograms were obtained in seven belugas, three males and four females, provisionally 2 to 7 years old. The measurements were performed using a transducer located 1 m in front of the head. The stimuli were tone pip trains of carrier frequencies ranging from 11.2 to 128 kHz with a pip rate of 1 kHz. Auditory evoked potentials (the rate following responses) were recorded from the head vertex. In majority of the subjects, Audiograms were similar to the typical odontocete Audiograms with the lowest thresholds (from 53.7 to 56.4 dB re 1 μPa) at mid-frequency range (from 32 to 64 kHz) and a sharp thresholds rise (up to 79.5 dB re 1 μPa) at high frequencies (90–128 kHz). One beluga (female, 6–7 years old) featured an asymmetric hearing loss within a frequency range from 22.5 to 54 kHz. The reason for the loss is the subject for not defined. The evoked potential Audiograms should be included into base screening of odontocete subjects involved in any kind of hearing research. ...

  • Comparison of directional selectivity of hearing in a beluga whale and a bottlenose dolphin
    The Journal of the Acoustical Society of America, 2009
    Co-Authors: Vladimir V. Popov, Alexander Ya Supin
    Abstract:

    Hearing thresholds as a function of sound-source azimuth were measured in a beluga whale Delphinapterus leucas and a bottlenose dolphin Tursiops truncatus in identical conditions using the auditory evoked-potential method. In both the beluga whale and bottlenose dolphin, the receiving beam width narrowed with frequency increase. At all frequencies, the receiving beam was markedly wider in the beluga whale than in the bottlenose dolphin. In particular, the 3-dB beam width in the beluga whale narrowed from +/-33.5 degrees at 8 kHz frequency to +/-14.3 degrees at 128 kHz; the 6-dB beam width narrowed from +/-56.9 degrees to +/-18.9 degrees , respectively. In the bottlenose dolphin, the 3-dB beam width decreased from +/-19.9 degrees at 8 kHz to +/-6.3 degrees at 128 kHz; the 6-dB beam width decreased from +/-33.1 degrees to +/-8.4 degrees, respectively. In the bottlenose dolphin, the axis of the low-frequency receiving beam deviated from the midline up to 15 degrees; in the beluga whale, this effect was not detected. The Audiograms of both the beluga whale and bottlenose dolphin were azimuth-dependent: from an Audiogram featuring the best sensitivity at intermediate frequencies at 0 degrees to that featuring monotonous threshold increase with frequency increase at 90 degrees. In the beluga whale, this dependence was less prominent than in the bottlenose dolphin.

  • behavioral and auditory evoked potential Audiograms of a false killer whale pseudorca crassidens
    Journal of the Acoustical Society of America, 2005
    Co-Authors: Michelle M L Yuen, Marlee Breese, Paul E Nachtigall, Alexander Ya Supin
    Abstract:

    Behavioral and auditory evoked potential (AEP) Audiograms of a false killer whale were measured using the same subject and experimental conditions. The objective was to compare and assess the correspondence of auditory thresholds collected by behavioral and electrophysiological techniques. Behavioral Audiograms used 3‐s pure-tone stimuli from 4to45kHz, and were conducted with a go∕no-go modified staircase procedure. AEP Audiograms used 20‐ms sinusoidally amplitude-modulated tone bursts from 4to45kHz, and the electrophysiological responses were received through gold disc electrodes in rubber suction cups. The behavioral data were reliable and repeatable, with the region of best sensitivity between 16 and 24kHz and peak sensitivity at 20kHz. The AEP Audiograms produced thresholds that were also consistent over time, with range of best sensitivity from 16to22.5kHz and peak sensitivity at 22.5kHz. Behavioral thresholds were always lower than AEP thresholds. However, AEP Audiograms were completed in a shorter ...

  • comparison of behavioral and auditory evoked potential aep Audiograms of a false killer whale pseudorca crassidens
    Journal of the Acoustical Society of America, 2004
    Co-Authors: Michelle M L Yuen, Marlee Breese, Paul E Nachtigall, Alexander Ya Supin
    Abstract:

    Behavioral and auditory evoked potential (AEP) Audiograms of a false killer whale were measured using the same subject and experimental conditions from 2001 to 2004. The objective was to compare and assess the validity of auditory thresholds collected by psychometric and electrophysiological techniques. Behavioral Audiograms used 3‐s pure‐tone stimuli from 4 to 45 kHz. AEP Audiograms used 20‐ms sinusoidally amplitude‐modulated (SAM) tone bursts from 4 to 45 kHz. Electrophysiological responses were received through gold disk electrodes mounted in rubber suction cups placed on the animals dorsal surface. Psychometric data were reliable and repeatable with the region of best sensitivity for the behavioral Audiograms between 16 and 24 kHz, and with peak sensitivity at 20 kHz. The AEP measures produced thresholds that were consistent over time, with ranges of best sensitivity from 16 to 22.5 kHz and peak sensitivity at 22.5 kHz. Behavioral thresholds were lower than AEP thresholds. Signal type and duration dif...