1aPP44 – What’s That Noise? The Effect of Hearing Loss and Tinnitus on Soldiers Using Military Headsets

Candice Manning, AuD, PhD – Candice.Manning@va.gov
Timothy Mermagen, BS – timothy.j.mermagen.civ@mail.mil
Angelique Scharine, PhD – angelique.s.scharine.civ@mail.mil

Human and Intelligent Agent Integration Branch (HIAI)
Human Research and Engineering Directorate
U.S. Army Research Laboratory
Building 520
Aberdeen Proving Ground, MD

Lay language paper 1aPP44, “Speech recognition performance of listeners with normal hearing, sensorineural hearing loss, and sensorineural hearing loss and bothersome tinnitus when using air and bone conduction communication headsets”
Presented Monday Morning, May 23, 2016, 8:00 – 12:00, Salon E/F
171st ASA Meeting, Salt Lake City

Military personnel are at high risk for noise-induced hearing loss due to the unprecedented proportion of blast-related acoustic trauma experienced during deployment from high-level impulsive and continuous noise (i.e., transportation vehicles, weaponry, blast-exposure).  In fact, noise-induced hearing loss is the primary injury of United States Soldiers returning from Afghanistan and Iraq.  Ear injuries, including tympanic membrane perforation, hearing loss, and tinnitus, greatly affect a Soldier’s hearing acuity and, as a result, reduce situational awareness and readiness.  Hearing protection devices are accessible to military personnel; however, it has been noted that many troops forego the use of protection believing it may decrease circumstantial responsiveness during combat.

Noise-induced hearing loss is highly associated with tinnitus, the experience of perceiving sound that is not produced by a source outside of the body.  Chronic tinnitus causes functional impairment that may result in a tinnitus sufferer to seek help from an audiologist or other healthcare professional.  Intervention and management are the only options for those individuals suffering from chronic tinnitus as there is no cure for this condition.  Tinnitus affects every aspect of an individual’s life including sleep, daily tasks, relaxation, and conversation to name only a few.  In 2011, the United States Government Accountability Office report on noise indicated that tinnitus was the most prevalent service-connected disability.  The combination of noise-induced hearing loss and the perception of tinnitus could greatly impact a Soldier’s ability to rapidly and accurately process speech information under high-stress situations.

The prevalence of hearing loss and tinnitus within the military population suggests that Soldier use of hearing protection is extremely important. The addition of hearing protection into reliable communication devices will increase the probability of use among Soldiers.  Military communication devices using air and bone-conduction provide clear two-way audio communications through a headset and a microphone.

Air conduction headsets offer passive hearing protection from high ambient noise, and talk-through microphones allow the user to engage in face-to-face conversation and hear ambient environmental sounds, preserving situation awareness.  Bone-conduction technology utilizes the bone-conduction pathway and presents auditory information differently than air-conduction devices (see Figure 1).  Because headsets with bone conduction transducers do not cover the ears, they allow the user to hear the surrounding environment and the option to communicate over a radio network.  Worn with or without hearing protection, bone conduction devices are inconspicuous and fit easily under the helmet.   Bone conduction communication devices have been used in the past; however, as newer devices have been designed, they have not been widely adopted for military applications.

Manning1a - headsetsA. Manning1b - headsetsB.

Figure 1. Air and Bone conduction headsets used during study: a) Invisio X5 dual in-ear headset and X50 control unit and b) Aftershockz Sports 2 headset.

Since many military personnel operate in high noise environments and with some degree of noise induced hearing damage and/or tinnitus, it is important to understand how speech recognition performance might be altered as a function of headset use.  This is an important subject to evaluate as there are two auditory pathways (i.e., air-conduction pathway and bone-conduction pathway) that are responsible for hearing perception.  Comparing the differences between the air and bone-conduction devices on different hearing populations will help to describe the overall effects of not only hearing loss, an extremely common disability within the military population, but the effect of tinnitus on situational awareness as well.  Additionally, if there are differences between the two types of headsets, this information will help to guide future communication device selection for each type of population (NH vs. SNHL vs. SNHL/Tinnitus).

Based on findings from speech understanding in noise literature, communication devices do have a negative effect on speech intelligibility within the military population when noise is present.  However, it is uncertain as to how hearing loss and/or tinnitus effects speech intelligibility and situational awareness under high-level noise environments.  This study looked at speech recognition of words presented over AC and BC headsets and measured three groups of listeners: Normal Hearing, sensorineural hearing impaired, and/or tinnitus sufferers. Three levels of speech-to-noise (SNR=0,-6,-12) were created by embedding speech items in pink noise.  Overall, performance was marginally, but significantly better for the Aftershockz bone conduction headset (Figure 2).  As would be expected, performance increases as the speech to noise ratio increases (Figure 3).

Manning2

Figure 2. Mean rationalized arcsine units measured for each of the TCAPS under test.

Manning3

Figure 3. Mean rationalized arcsine units measured as a function of speech to noise ratio.

One of the most fascinating things about the data is that although the effect of hearing profile was significant, it was not practically so, the means for the Normal Hearing, Hearing Loss and Tinnitus groups were 65, 61, and 63, respectively (Figure 4).  Nor was there any interaction with any of the other variables under test.  One might conclude from the data that if the listener can control the level of presentation, the speech to noise ratio has about the same effect, regardless of hearing loss. There was no difference in performance with the TCAPS due to one’s hearing profile; however, the Aftershockz headset provided better speech intelligibility for all listeners.

Manning4

Figure 4. Mean rationalized arcsine units observed as a function of the hearing profile of the listener.

2aAAa7 – Gunshot recordings from a criminal incident: who shot first?

Robert C. Maher – rob.maher@montana.edu
Electrical & Computer Engineering Department
Montana State University
P.O. Box 173780
Bozeman, MT 59717-3780

Popular version of paper 2aAAa7, “Gunshot recordings from a criminal incident: Who shot first?”
Presented Tuesday morning, May 24, 2016, 10:20 AM, Salon E
171st ASA Meeting, Salt Lake City

In the United States, criminal actions involving firearms are of ongoing concern to law enforcement and the public.  The FBI’s 2013 National Incident-Based Reporting System (NIBRS) report lists 50,721 assault incidents and 30,915 robbery incidents involving firearms that year [1].

As more and more law enforcement officers wear vest cameras and more and more citizens carry smartphones, the number of investigations involving audio forensic evidence continues to grow—and in some cases the audio recordings may include the sound of gunshots.

Is it possible to analyze a forensic audio recording containing gunshot sounds to discern useful forensic evidence?  In many cases the answer is yes.

Audio forensics, or forensic acoustics, involves evaluation of audio evidence for either a court of law or for some other official investigation [2].  Experts in audio forensics typically have special knowledge, training, and experience in the fields of acoustics, electrical engineering, and audio signal processing.

One common request in audio forensic investigations involving gunshots is “who fired first?”  There may be a dispute about the circumstances of a firearms incident, such as one party claiming that shots were fired in self-defense after the other party fired first, while the other party has the opposite claim.  Sometimes a dispute can arise if a witness reports that a law enforcement officer shot an armed but fleeing suspect without justification, while the officer claims that the suspect had fired.

maher_fig1

Figure 1: Muzzle blast recording of a 9mm handgun obtained under controlled conditions [4].

The sound of a gunshot is often depicted in movies and computer games as a very dramatic “BOOM” sound that lasts for as long as a second before diminishing away.  But the actual muzzle blast of a common handgun is really only about 1 millisecond (one 1/1000th of a second) in duration (see Figure 1).  More than 20-30 meters away, most of the audible sound of a gunshot is actually sound waves reflected by nearby surfaces [3].

Let’s consider a simplified case example from an investigation in an unnamed jurisdiction.  In this case, a shooting incident on a city street involving two perpetrators was recorded by a residential surveillance system located down the street.  The camera’s field-of-view did not show the incident, but the microphone picked up the sounds of gunfire.  Based on witness reports and the identification of shell casings and other physical evidence at the scene, the police investigators determined that the two perpetrators were several meters apart and fired their handguns toward each other, one pointing north and the other pointing south.  Figuring out which gun was fired first could not be determined from the physical evidence at the scene nor from witness testimony, so attorneys for the suspects requested analysis of the audio recording to determine whether or not it could help answer the “who shot first?” question.

The waveform and the corresponding spectrogram from the portion of the recording containing the first two gunshot sounds are shown in Figure 2.  The spectrogram is a special kind of graph that depicts time on the horizontal axis and frequency on the vertical axis, with the brightness of the graph indicating the amount of sound energy present at a particular time in a particular frequency range.  The sound energy envelope for this same signal is shown in Figure 3.  The microphone picked up the direct sound of the gunshots, but also the reflected sound from the street, nearby buildings, and other obstacles, causing the relatively long duration of the two shots in the recording.

In this case, we note that the first gunshot has a distinctive echo (indicated by the arrow), while the second gunshot does not show this feature.  What might account for this peculiar difference?

maher_fig2

Figure 2:  Sound waveform and spectrogram of two gunshots recorded by a residential surveillance system.  The arrow indicates the distinctive echo.

maher_fig3

Figure 3:  Sound energy envelope for the two gunshots depicted in Figure 2.  The arrow indicates the echo.

Examining the neighborhood street where the shooting incident took place (Figure 4) revealed that there was a “T” intersection about 90 meters north of the shooting scene with a large building facing the street.  The length of the reflected sound path from the shooting site to the large building and back is therefore a little over 180 meters, which corresponds to the 0.54 seconds of time delay between the direct sound of the gunshot an the echo—which would account for the timing of the distinct reflection.  The microphone was located 30 meters south of the shooting scene.  But why would the observed reflection differ for the two firearms if they were located quite close together at the time of the gunfire?

maher_fig4

Figure 4:  Sketch of the shooting scene (plan view)

Our conclusion was that the firearm pointing north toward the “T” intersection would likely produce a stronger reflection because the muzzle blast of a handgun is louder in the direction the gun is pointing [5]. Thus, the gun pointing toward the reflecting surface would produce a stronger reflected sound than the gun pointing away from the reflecting surface.

While the availability of additional acoustic evidence of firearm incidents can only be a positive development for the U.S. justice system, interpreting audio recordings of gunshots remains a challenge for audio forensic examiners for several reasons. First, the acoustical characteristics of gunshots must be studied carefully because the recording is likely to include sound reflections, diffraction, reverberation, background sounds, and other content that can interfere with the analysis.  Second, common audio recorders are intended for speech signals, and therefore they are not designed to capture the very brief and very intense sounds of gunfire.  Finally, the acoustical similarities and differences among different types of firearms are still the subject of research, so the notion of having a simple database of gunshot sounds to compare with an evidentiary recording is not yet feasible.

 

[1]  U.S. Department of Justice, 2013 National Incident-Based Reporting System (NIBRS) Data Tables (2013). Available at https://www.fbi.gov/about-us/cjis/ucr/nibrs/2013/data-tables . Accessed May 6, 2016.

[2]  Maher, R.C., Lending an ear in the courtroom: forensic acoustics, Acoustics Today, vol. 11, no. 3, pp. 22-29, 2015.

[3]  Maher, R.C., Acoustical characterization of gunshots, Proceedings of the IEEE SAFE Workshop on Signal Processing Applications for Public Security and Forensics, Washington, DC, pp. 109-113 (2007).

[4]  Maher, R.C. and Shaw, S.R., Gunshot recordings from digital voice recorders, Proceedings of the Audio Engineering Society 54th Conference, Audio Forensics—Techniques, Technologies, and Practice, London, UK (2014).

[5]  Maher, R.C. and Shaw, S.R., Directional aspects of forensic gunshot recordings, Proceedings of the Audio Engineering Society 39th Conference, Audio Forensics—Practices and Challenges, Hillerød, Denmark (2010).

2aBAa7 – Ultrasonic “Soft Touch” for Breast Cancer Diagnosis

Mahdi Bayat – bayat.mahdi@mayo.edu
Alireza Nabavizadeh- nabavizadehrafsanjani.alireza@mayo.edu
Viksit Kumar- kumar.viksit@mayo.edu
Adriana Gregory- gregory.adriana@mayo.edu
Azra Aliza- alizad.azra@mayo.edu
Mostafa Fatemi- Fatemi.mostafa@mayo.edu
Mayo Clinic College of Medicine
200 First St SW
Rochester, MN 55905

Michael Insana- mfi@illinois.edu
University of Illinois at Urbana-Champaign
Department of Bioengineering
1270 DCL, MC-278
1304 Springfield Avenue
Urbana, IL 61801

Popular version of paper 2aBAa7, “Differentiation of breast lesions based on viscoelasticity response at sub-Hertz frequencies”
Presented Tuesday Morning, May 24, 2016, 9:30 AM, Snowbird/Brighton room
171st ASA Meeting, Salt Lake City

Breast cancer remains the first cause of death among American women under the age of 60. Although modern imaging technologies, such as enhanced mammography (tomosynthesis), MRI and ultrasound, can visualize a suspicious mass in breast, it often remains unclear whether the detected mass is cancerous or non-cancerous until a biopsy is performed.

Despite high sensitivity for detecting lesions, no imaging modality alone has yet been able to determine the type of all abnormalities with high confidence. For this reason most patients with suspicious masses, even those with very small likelihood of a cancer, opt in to undergo a costly and painful biopsy.

It is long believed that cancerous tumors grow in the form of stiff masses that, if found to be superficial enough, can be identified by palpation. The feeling of hardness under palpation is directly related to the tissue’s tendency to deform upon compression.  Elastography, which has emerged as a branch of ultrasound, aims at capturing tissue stiffness by relating the amount of tissue deformation under a compression to its stiffness. While this technique has shown promising results in identifying some types of breast lesions, the diversity of breast cancer types leaves doubt whether stiffness alone is the best discriminator for diagnostic purposes.

Studies have shown that tissues subjected to a sudden external force do not deform instantly, rather they deform gradually over a period of time. Tissue deformation rate reveals another important aspect of its mechanical property known as viscoelasticity. This is the main material feature that, for example, makes a piece of memory foam to feel differently from a block of rubber under the touch. Similar material feature can be used to explore mechanical properties of different types of tissue. In breast masses, studies have shown that biological pathways leading to different breast masses are quite different. While in benign lesions an increase in a protein-based component can potentially increase its viscosity, hence a slower deformation rate compared to normal tissue, the opposite trend occurs in malignant tumors.

In this study, we report on using an ultrasound technique that enables capturing the deformation rate in breast tissue. We studied 43 breast masses in 42 patients and observed that a factor based on the deformation rate was significantly different in benign and malignant lesions (Fig. 1).

The results of this study promise a new imaging biomarker for diagnosis of the breast masses. If such technique proves to be of high accuracy in a large pool of patients, then this technology can be integrated into breast examination procedures to improve the accuracy of diagnosis, reduce unnecessary biopsies, and help detecting cancerous tumors early on

Figure 1 breast cancer

Figure1- Distribution of relative deformation rates for malignant and benign breast lesions. A significantly different relative deformation rates can be observed in the two groups, thus allowing differentiation of such lesions.

5aMU5 – Guitar String Sound Retrieved from Moving Pixels

Bożena Kostek – bokostek@audioakustyka.org
Audio Acoustics Laboratory
Faculty of Electronics
Telecommunications and Informatics
Gdansk University of Technology, Narutowicza 11/12
80-233 Gdansk, Poland

Piotr Szczuko – szczuko@sound.eti.pg.gda.pl
Józef Kotus – Joseph@sound.eti.pg.gda.pl
Maciej Szczodrak – szczodry@sound.eti.pg.gda.pl
Andrzej Czyżewski – andcz@sound.eti.pg.gda.pl
Multimedia Systems Department, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland

Popular version of paper 5aMU5, “Vibration analysis of acoustic guitar string employing high-speed video cameras”
Presented Friday morning, May 28, 2016, 9:00, Solitude Room
171st ASA Meeting, Salt Lake City

The aim of this study was to develop a method of visual recording and analyzing the vibrations of guitar strings using high-speed cameras and dedicated video processing algorithms. The recording of a plucked string reveals the way in which the deformations propagate, composing the standing and travelling wave. The paper compares the results for a few selected models of classical and acoustic guitars, and it involves processing the vibration image into to the sound recording. The sound reconstructed in this way is compared with the sound recorded synchronously with the reference measurement microphone.

MEASUREMENT SETUP AND METHOD OF VIBRATIONS RECORDING
The measurements were made for three different models and types of guitars (Fig. 1a,b,c). The Martin D-45 is one of the best mass produced acoustic guitars in the world. Its top plate is made from spruce, its sides and back from Indian rosewood, its neck from mahogany, and its fingerboard from ebony. The guitar shape is of the Dreadnought type. In the experiments, acoustic strings were used, metal, thickness of 0.52.

Classical guitar model: MGP 145 classic, c, ar, tailpiece, prototype model. Made in 2014 by Sergei Stańczuk in SEGA Luthier Guitar Studio in Warsaw as a prototype model of classical guitar with two tailpieces. In the experiments, acoustic strings were used, metal, thickness of 0.52.

Defil guitar is a classical instrument made in 1978 by a Polish company, designed for amateur players. In the experiments, the classic nylon strings were used, with a thickness of 0.44.

a) b)Kostek_et_al_Fig.1b - Guitar c)Kostek_et_al_Fig.1c - Guitar

Fig. 1. Guitars under research: a) Martin Dreadnought D-45 acoustic guitar, b) SEGA MGP 145 classic c) classical guitar Defil

Acoustic guitars can be tested applying the acoustic methods and recording of the emitted sound, mathematical modeling and simulation (including the finite element method) or direct vibration measurement using various vibrometric methods (laser, piezoelectric transducer, electromagnetic transducer, an analogue movement meter or digital high-speed cameras and optical displacement and deformation measurement).

Fig. 2 shows the layout of the experimental setup. Video tracking and measurement of vibration are made through the use of two identical and synchronized cameras, acquiring 1,600 frames per second video with a resolution of 2000×128 pixels, and the exposure time of 100ms. A high-class measurement microphone along with an acquisition system were used for the audio recording simultaneously with video shooting. The cameras are placed side by side and oriented towards: a foothold in the bridge, the area above the opening of the sound hole, the neck section up to fret 19 (the first camera) and a section of the neck from fret 19 to fret 6 (second camera).

Kostek_et_al_Fig.2 - Guitar

Fig. 2. Setup for the video and audio recordings of the strings vibrations.

METHOD OF RECONSTRUCTING SOUND FROM IMAGE
In order to accurately measure the deformation of the strings, the video analysis algorithm was created to determine the position of the elementary section of the string visible in each column of the image. Recording, lighting, and exposure conditions were to ensure that the string was the brightest part of the image, and the result of would only be one pixel. The results of the analysis from both cameras, i.e. two vectors describing the position of the string sections were combined into a single series.

It was noticed that the string at rest acted on the bridge with the strength of its tension. Stimulation of the strings was associated with its deformation – increased stress and delivery of energy. After a substantial simplification of the analysis it was possible to perform a simple summation of the deviations of each point on the string and the conversion of the value obtained into a sound sample for each video frame.

ANALYSIS OF SOUND
Analysis of the averaged spectra highlights the differences between the image acquired and microphone recorded sound (Fig. 3.) Spectra were scaled so as that the amplitude of the first harmonic f = 110 Hz was equal for both recordings.

Martin and Luthier guitars (Fig. 3a, 3b) had very thick acoustic strings, which do not deflect much. Defil guitar (Fig. 3c) has soft strings for classical play that easily deform and vibrate with a large amplitude. The colors of generated sounds are different: the ratio between the harmonics is not maintained. This is due to the participation of the soundboard in the generation of sound.

a)Kostek_et_al_Fig.3a b)Kostek_et_al_Fig.3b c)Kostek_et_al_Fig.3c

Fig. 3. Comparison of the average spectra for the signals obtained from the microphone and reconstructed by an optical method: a) Martin Dreadnought D-45 acoustic guitar, b) SEGA MGP 145 classic c) classical guitar DefilCONCLUSION

A method of obtaining the string deformation characteristics from an image and acquiring sound samples from the observed vibrations was presented. Significant differences resulting from not taking into account the impact of soundboard were observed, therefore further work in this area will focus on the systematic study of differences in the spectra and modelling the participation of the guitar soundboard in the creation of sound.

ACKNOWLEDGEMENTS
This research study was supported by the grant, funded by the Polish National Science Centre, decision number DEC-2012/05/B/ST7/02151.

The authors wish to thank Chris Gorski and Sergiusz Stańczuk for providing the guitars.

3pSC10 – Does increasing the playback speed of men’s and women’s voices reduce their intelligibility by the same amount?

Eric M. Johnson – eric.martin.johnson@utah.edu
Sarah Hargus Ferguson – sarah.ferguson@hsc.utah.edu

Department of Communication Sciences and Disorders
University of Utah
390 South 1530 East, Room 1201
Salt Lake City, UT 84112

Popular version of poster 3pSC10, “Gender and rate effects on speech intelligibility.”
Presented Wednesday afternoon, May 25, 2016, 1:00, Salon G
171st ASA Meeting, Salt Lake City

Older adults seeking hearing help often report having an especially hard time understanding women’s voices. However, this anecdotal observation doesn’t always agree with the findings from scientific studies. For example, Ferguson (2012) found that male and female talkers were equally intelligible for older adults with hearing loss. Moreover, several studies have found that young people with normal hearing actually understand women’s voices better than men’s voices (e.g. Bradlow et al., 1996; Ferguson, 2004). In contrast, Larsby et al. (2015) found that, when listening in background noise, groups of listeners with and without hearing loss were better at understanding a man’s voice than a woman’s voice. The Larsby et al. data suggest that female speech might be more affected by distortion like background noise than male speech is, which could explain why women’s voices may be harder to understand for some people.

We were interested to see if another type of distortion, speeding up the speech, would have an equal effect on the intelligibility of men and women. Speech that has been sped up (or time-compressed) has been shown to be less intelligible than unprocessed speech (e.g. Gordon-Salant & Friedman, 2011), but no studies have explored whether time compression causes an equal loss of intelligibility for male and female talkers. If an increase in playback speed causes women’s speech to be less intelligible than men’s, it could reveal another possible reason why so many older adults with hearing loss report difficulty understanding women’s voices. To this end, our study tested whether the intelligibility of time-compressed speech decreases for female talkers more than it does for male talkers.

Using 32 listeners with normal hearing, we measured how much the intelligibility of two men and two women went down when the playback speed of their speech was increased by 50%. These four talkers were selected based on their nearly equivalent conversational speaking rates. We used digital recordings of each talker and made two different versions of each sentence they spoke: a normal-speed version and a fast version. The software we used allowed us to speed up the recordings without making them sound high-pitched.

Audio sample 1: A sentence at its original speed.

Audio sample 2: The same sentence sped up to 50% faster than its original speed.

All of the sentences were presented to the listeners in background noise. We found that the men and women were essentially equally intelligible when listeners heard the sentences at their original speed. Speeding up the sentences made all of the talkers harder to understand, but the effect was much greater for the female talkers than the male talkers. In other words, there was a significant interaction between talker gender and playback speed. The results suggest that time-compression has a greater negative effect on the intelligibility of female speech than it does on male speech.

johnson & ferguson fig 1

Figure 1: Overall percent correct key-word identification performance for male and female takers in unprocessed and time-compressed conditions. Error bars indicate 95% confidence intervals.

These results confirm the negative effects of time-compression on speech intelligibility and imply that audiologists should counsel the communication partners of their patients to avoid speaking excessively fast, especially if the patient complains of difficulty understanding women’s voices. This counsel may be even more important for the communication partners of patients who experience particular difficulty understanding speech in noise.

 

  1. Bradlow, A. R., Torretta, G. M., and Pisoni, D. B. (1996). “Intelligibility of normal speech I: Global and fine-grained acoustic-phonetic talker characteristics,” Speech Commun. 20, 255-272.
  2. Ferguson, S. H. (2004). “Talker differences in clear and conversational speech: Vowel intelligibility for normal-hearing listeners,” J. Acoust. Soc. Am. 116, 2365-2373.
  3. Ferguson, S. H. (2012). “Talker differences in clear and conversational speech: Vowel intelligibility for older adults with hearing loss,” J. Speech Lang. Hear. Res. 55, 779-790.
  4. Gordon-Salant, S., and Friedman, S. A. (2011). “Recognition of rapid speech by blind and sighted older adults,” J. Speech Lang. Hear. Res. 54, 622-631.
  5. Larsby, B., Hällgren, M., Nilsson, L., and McAllister, A. (2015). “The influence of female versus male speakers’ voice on speech recognition thresholds in noise: Effects of low-and high-frequency hearing impairment,” Speech Lang. Hear. 18, 83-90.