You too choose two YouTubes…

In two previous blog posts we discussed a mixed picture of findings for the relationship between audio quality and real world usage/popularity of audio files on the website Freesound. In one of our Web experiments, Audiobattle, we found that the number of downloads for recordings of birdsong predicted independent ratings of quality reasonably well. In a follow up experiment, however, we found that this effect did not generalise well to other categories of sound – there was almost no relationship between quality ratings and the number of plays or downloads for recordings of thunderstorms or Church bells, for example.

For our next Web test, Qualitube, we reasoned that people might find it easier to compare samples if they were recordings of the same event. Continue reading

The Good Recorder iPhone app

Good news! Today sees the launch of the project’s first ever app – The Good Recorder. Absolutely free and available now via the iTunes store, or click here.

What is The Good Recorder?

Screenshot 2014-02-14 15.39.36The Good Recorder is a sound recording app (currently only for iOS 7 devices) designed to help users achieve high quality audio recordings by monitoring for common recording errors and providing feedback about them. Currently the app incorporates findings and algorithms from our previous work with wind noise. The plan is to further develop the app with auto-detection of handling noise and distortion as our research in these areas progresses. Continue reading

Wind noise recordings – Validating a Wind Noise Detector

Array of microphones used to capture wind noise

Array of microphones used to capture wind noise

After developing a microphone wind noise detector which is trained on simulated examples of wind noise (see my ICME conference paper),  rigorous proof of the algorithm’s success (or failure!) is required.  In fact the reviewers of this aforementioned paper suggested this.  To that aim I packed a car full with microphone stands, cables, preamps, and a number of recording devices and set off to collect some examples of wind noise.

The requirement for the location to collected these examples is that there is very low levels of background noise.  I found a location up upon Rivington Pike, north of Manchester.  There was a road which was closed for repair, ideal! as it means no traffic.  After a couple of false starts and some help from a kindly local man, I found a good location with, no road, rail, urban or air traffic noise.  I located a place away from trees, which can create a surprisingly loud level of rustling noise and set my microphones up.

Array of microphones used to capture wind noise

Array of microphones used to capture wind noise

Array of microphones used to capture wind noise

Array of microphones used to capture wind noise

I was using an Edirol R-44 to capture four channel of audio onto an SD card at 44.1 kHz sampling frequency.  I set up two measurement microphones, one with a wind shield, a sure SM58 dynamic microphone, a zoom H2 recorder and an iPhone taped to a stand.  Though one of my microphones sported a windshield, due to the particularly blustery conditions with 20 mph winds, wind noise was present on all recordings.  This made it all the more important that the background sound level was as low as possible as I intend to compute the wind noise level, assuming that the background noise level is negligible.

recording device used, 4 channels
recording device used, 4 channels
Calibration was carried out on the two measurement microphones by placing a calibrator on each, playing a 1 kHz tone at around 94dB and recording these sounds.  Now I can calibrate my recordings so that I can present data in the actual sound pressure levels recorded for these two microphones.  To calibrate the other devices is a little tricky, but a 1 kHz tone was played back over a loudspeaker at approx 1m distance and recorded on all devices simultaneously.  As I can now know the true sound pressure level from the calibrated measurement microphones, i can also compute the true level of this tone relative to the calibrated recordings and using this information calibrate the other microphones to within a few decibels.  To remove wind noise a narrow band-pass filter is applied centered on 1 kHz. Clearly there is some error due to the location of the microphones and and residual wind noise present within the pass-band, but this is not a significant problem.
Several hours later, and I am rather cold but have the data, now back to Salford set up my validation procedure.