Greetings All, As with many *coincidences* in the universe, the recent discussions regarding the quaudio mic were informative and timely. From what I gathered, there are certain mic techniques and algorithms that are better suited for the recording of STATIONARY sound sources than for MOVING sources. This, then, made me realize that recording moving sound sources is no trivial task; particularly when it comes to the reconstruction of an auditory event. I have the idea that certain sounds can be separated from noise by not only their spectral characteristics and spatial location, but also by their perceived motion. I’m not referring to judging the distance of the moving object, or its direction of travel. An analogy to vision would be camouflage: Despite relatively poor vision, I often detect wee critters such as lizards, toads, and insects because of their motion. There’s no way I could otherwise see them against a backdrop of similarly colored terrain. The first step towards identifying the critter is realizing that it’s there to begin with. I now have a reason for incorporating auditory *motion* in a research project. Maybe a few of you would like to join in or provide assistance. I’ll confess that I could use a project to get me a step closer to acceptance into a doctoral program. Furthermore, there’s a conference happening October 15-18 in Toronto: It’s the 8th Objective Measures Symposium on Auditory Implants. Their theme is ‘Unique people, unique measures, unique solutions’ reflecting a collective goal of providing the best hearing for persons needing an auditory prostheses (= cochlear and brainstem implants). Below are a few of my ideas (egad!) and thoughts: I know I’ve said this more than once, but I’m not too keen on presenting 5-word sentences presented in a background of pink noise as an *objective* measure of cochlear implant (CI) efficacy. This is may be objective in telling us how well a person performs while seated in a surround of pink noise and listening to nonsensical sentences, but so what? I’ve been hoping to present or propose a slightly *better* yardstick, even if there’s no past or standard reference to pit my data against. I had previously proposed adding video to complete the AzBio, IEEE, and SPIN sentences (currently used for speech audiometry), and I know of at least one doctoral student who has taken this to heart. What I now propose (with video) are sound-source identification tasks that can be *objectively* scored. Simple sounds may not be readily identifiable by the hearing impaired. This isn’t new news, but how well stationary and mobile sounds can be identified by an implant wearer could be of value, particularly when designing implant algorithms. Imagine listening to several sounds through a 12-channel (max) vocoder. This roughly approximates CI listening. Your pitch discrimination ability is largely shot to hell, and dynamics would be compromised if compression were also added. Sounds emanating from various sources would be blurred, but hopefully your binaural sense of direction provides some signal-from-noise segregation. You still detect rhythm... at least for repetitive sounds. Given the above, we might go a step further (in the direction of Ecological Psychology) and ask whether we can tell a difference from tearing or breaking sounds, water drops from other temporal-patterned sounds, or rolling sounds from steady-state noises. Wind, although around us and a type of motion, is stationary relative to, say, a rolling object. When heard through a vocoder, they may be indistinguishable... unless the perceived motion of the rolling object provides useful information. Given a closed set of choices to choose from (and perhaps visual context), we could determine *objectively* how well we identify sounds presented in a background of other sounds. The latter is the *new* part: Can we segregate and then identify, sounds because of their motion, spectral make-up, etc. despite minimal or distorted information? I would prefer to create stimuli from real-world sounds, though panning monaural sounds could be of some help. I like the *naturalness* of Ambisonic recordings, but now question how well they can be reproduced. I know that there are recordings of airplanes and helicopters (recorded by Paul Hodges, John Leonard orAaron Heller? I can’t find names/recordings online), so I have no doubt that Ambisonics is a viable method of recording moving sound sources. I am, however, concerned about the limitations, and how many sounds (to include sounds’ reflections) can be reproduced without raising doubt as to the *accuracy* of the playback. I believe this is a do-able project that could provide meaningful information. Fine tuning implants to deal with an *outside* world could be different from the algorithms used to perfect speech understanding. Best, Eric C. -------------- next part -------------- An HTML attachment was scrubbed... URL: <https://mail.music.vt.edu/mailman/private/sursound/attachments/20130423/d415c366/attachment.html> _______________________________________________ Sursound mailing list Sursound@music.vt.edu https://mail.music.vt.edu/mailman/listinfo/sursound
