4aPP7 – “The Loudness of an Auditory Scene” – William A. Yost

The Loudness of an Auditory Scene

William A. Yost – william.yost@asu.edu

Michael Torben Pastore – m.torben.pastore@gmail,edu

Speech and Hearing Science

Arizona State University

PO Box 870102

Tempe AZ, 85287-0102

 

Popular version of paper 4aPP7

Presented Thursday morning, May 10, 2018

175th ASA Meeting, Minneapolis, MN

This paper is part of special session honoring Dr. Neil Viemeister, University of Minnesota, for his brilliant career. One of the topics Dr. Viemeister studies is loudness perception. Our presentation deals with the perceived loudness of an auditory scene when several people talk at about the same time. In the real world, the sounds of all the talkers are combined into one complex sound before they reach a listener’s ears. The auditory brain sorts this single complex sound into acoustic “images“, where each image represents the sound of one of the talkers. In our research, we try to understand how many such images can be ”pulled out” of an auditory scene so that they are perceived as separate, identifiable talkers.

In one type of simple experiment listeners are asked to determine how many more talkers it takes for listeners to notice that the number of talkers has increased. When we increase the number of talkers, the additional talkers make the overall sound louder and the change in loudness can be used as a cue to help listeners discriminate which sound has more talkers. If we make the overall loudness of a four-talker scene (as an example) and a six-talker scene (as an example) the same, the loudness of the individual talkers in the six-talker scene will be less than the loudness of the individual talkers in the four-talker scene.

If listeners can focus on the individual talkers in the two scenes, they might be able to use the change in loudness of individual talkers as a cue for discrimination. If listeners cannot focus on individual talkers in a scene, then the two scenes may not be discriminable and they are likely to be judged as equally loud. We have found that listeners can make loudness judgments of the individual talkers for scenes of two or three talkers, but not more. This indicates that the loudness of a complex sound may depend on how well the individual components of the sound are perceived and, if so, that only two or three such components (images, talkers) can be processed by the auditory brain at a given time.

Trying to listen to one or more people in a situation of many people talking at the same time is difficult, especially for people who are hard of hearing. If the normal auditory system can only process a few sound sources presented at the same time, this reduces the complexity of devices (e.g., hearing aids) that might be designed to help people with hearing impairment process sounds in complex acoustic environments. In auditory virtual reality (AVR) scenarios, there is a computational cost associated with processing each sound source. If an AVR system only has to process a few sound sources to mimic normal hearing, it would be a lot less expensive than if the system has to process many sound sources.  (Supported by grants from National Institutes of Health, NIDCD and Oculus VR, LLC)

2aEA3 – Insect Ears Inspire Miniature Microphones – James Windmill

Insect Ears Inspire Miniature Microphones

 

James Windmill – james.windmill@strath.ac.uk

University of Strathclyde

204 George Street

Glasgow, G1 1XW

United Kingdom

 

Popular version of paper 2aEA3

Presented Tuesday morning, May 8, 2018

175th ASA Meeting, Minneapolis, MN

 

Miniature microphones are a technology that everyone uses everyday without thinking about it. They are used in smartphones, laptops, tablets, and more recently in smart home equipment. However, working with sound technology always means there are issues, like how to deal with background noise. Engineers have always looked for ways to make technology better, and in miniature microphones one of the paths for improvement has been to look at how insects hear. If you want to design a really small microphone, then why not look at how the ear of a really small animal works?

 

In the 1990’s researchers discovered that a small fly (Ormia ochracea) had a very directional ear. That is, it can tell the direction that sound was coming from with a lot higher accuracy than predicted. Since that discovery many engineers have made attempts to make microphones copying the mechanism in the Ormia ear. Much of the effort has spent been trying to get round the problem that the Ormia is only interested in hearing one specific frequency. Humans want microphones that cover all the frequencies we can hear. Why bother copying this insect ear? If you could make a tiny directional microphone then a lot of background noise drops simply because the microphone points towards the person speaking.

 

At Strathclyde we have developed a variety of microphones based on the Ormia ear mechanism. The main push in this work has been to try and get more sensitive microphones working across more frequencies. To do this we have put four microphones into one Ormia type design, as in Figure 1. So instead of a single frequency, the microphone works as a miniature directional microphone across four main frequencies [1].

Figure 1. Four frequency Ormia inspired miniature microphone.

 

Work on the Ormia system at Strathclyde encouraged us to think of other things that insect ears do, and their structure, to see if there are other advantages to find. This work has taken two main themes. Firstly, many hearing systems in nature are not just simple mechanical systems; they are active sensors. That is they change how they function depending on what sound they’re listening to. So for a quiet sound they increase the amplification of the signal in the ear, or for a loud sound they turn it down. Some ears also change their frequency response, changing the frequencies they are tuned to. Strathclyde researchers have taken these ideas and produced miniature microphone systems that can do the same thing [2]. Why do this, when you can just do it in signal processing? By making the microphone “smart” you can free up processor power to do other things, or reduce the delay between a sound arriving and the electronic signal being used.

Figure 2. Graphs showing the results of a miniature microphone actively changing its frequency (A) and gain response (B).

 

Secondly, we thought about how you make miniature microphones. The ones we use in phones, computers etc today are made using computer chip technology, so are made very flat out of very hard silicon. Insect ears are made of a relatively soft material, and come in a huge variety of three dimensional shapes. The obvious thing it seemed to us was to try making insect inspired microphones using 3D printing techniques. This is very early work, its not easy to do. But we have had some success making microphone sensors using 3D printers [3]. Figure 3 shows an “acoustic sensor” that was inspired by how the locust hears sound.

Figure 3. 3D printed acoustic sensor inspired by the ear of a locust.

 

There is still a lot of work to do, both on developing these techniques and technologies, and on working out how best to use them in everyday technologies like the smartphone. Then again, a huge number of different insects have ears, each working in slightly different ways to hear different things for different reasons, so there are a lot of ears out there we can take inspiration from.

 

[1] Bauer R et al. (2017), Housing influence on multi-band directional MEMS microphones inspired by Ormia ochracea, IEEE Sensors Journal, 17: 5529-5536.

http://dx.doi.org/10.1109/JSEN.2017.2729619

 

[2] Guerreiro J et al. (2017), Simple Ears Inspire Frequency Agility in an Engineered Acoustic Sensor System, IEEE Sensors Journal, 17: 7298-7305.

http://dx.doi.org/10.1109/JSEN.2017.2699697

 

[3] Domingo-Roca R et al. (2018), Bio-inspired 3D-printed piezoelectric device for acoustic frequency selection, Sensors & Actuators: A. Physical, 271: 1-8.

https://doi.org/10.1016/j.sna.2017.12.056

 

4aSC12 – “When it comes to recognizing speech, being in noise is like being old” – Kristin Van Engen

When it comes to recognizing speech, being in noise is like being old”

 

Kristin Van Engen – kvanengen@wustl.edu

Washington University in St. Louis

1 Brookings Drive

St. Louis, MO 63130

 

Avanti Dey

Washington University in St. Louis

1 Brookings Drive

St. Louis, MO 63130

 

Nichole Runge

Washington University in St. Louis

1 Brookings Drive

St. Louis, MO 63130

 

Mitchell Sommers

Washington University in St. Louis

1 Brookings Drive

St. Louis, MO 63130

 

Brent Spehar

Washington University in St. Louis

1 Brookings Drive

St. Louis, MO 63130

 

Jonathen E. Peelle

Washington University in St. Louis

1 Brookings Drive

St. Louis, MO 63130

 

Popular version of paper 4aSC12

Presented Thursday morning, May 10, 2018

175th ASA Meeting, Minneapolis, MN

 

How hard is it to recognize a spoken word?

Well, that depends. Are you old or young? How is your hearing? Are you at home or in a noisy restaurant? Is the word one that is used often, or one that is relatively uncommon? Does it sound similar to lots of other words in the language?

As people age, understanding speech becomes more challenging, especially in noisy situations like parties or restaurants. This is perhaps unsurprising, given the large proportion of older adults who have some degree of hearing loss. However, hearing measurements do not actually do a very good job of predicting the difficulty a person will have with speech recognition, and older adults tend to do worse than younger adults even when their hearing is good.

We also know that some words are more difficult to recognize than others. Words that are used rarely are more difficult than common words, and words that sound similar to many other words in the language are recognized less accurately than unique-sounding words. Relatively little is known, however, about how these kinds of challenges interact with background noise to affect the process of word recognition or how such effects might change across the lifespan.

In this study, we used eye tracking to investigate how noise and word frequency affect the process of understanding spoken words. Listeners were shown a computer screen displaying four images, and listened the instruction “Click on the” followed by a target word (e.g., “Click on the dog.”). As the speech signal unfolds, the eye tracker records the moment-by-moment direction of the person’s gaze (60 times per second). Since listeners direct their gaze toward the visual information that matches incoming auditory information, this allows us to observe the process of word recognition in real time.

Our results indicate that word recognition is slower in noise than in quiet, slower for low-frequency words than high-frequency words, and slower for older adults than younger adults. Interestingly, young adults were more slowed down by noise than older adults. The main difference, however, was that young adults were considerably faster to recognize words in quiet conditions. That is, word recognition by older adults didn’t differ much from quiet to noisy conditions, but young listeners looked like older listeners when tasked with listening to speech in noise.