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Posts tagged: Wind Noise

Wind noise meter used to improve bird song recognition app.

9 October 2014

Recently we had an email from a company who are developing a bird song recognizer who were having problems with wind noise corrupting recordings and giving inaccurate results.  The company,  iSpiny was interested in using our code for real time wind noise detection to indicate when high levels of wind noise would cause problems with their algorithm.  So while not directly related to audio quality it shows that our research has a wider possible application.  As we understand the wind noise detector is now being utilized within the mobile bird song recognizer app .  For more information see the following site;

http://www.spinysoft.co.uk/ispiny.html

if you are interested in using the algorithm with your own application there is the offline batch detector here,

https://github.com/kenders2000/WindNoiseDetection/

as well as a realtime method implemented for iPhone. (contact us for details)

 

Microphone wind noise – published in the Journal of the acoustical society of america paper

11 September 2014

Our work into the perception and automated detection of microphone wind noise had been published in the Journal of The Acoustical Society of America. This paper discuss how wind noise is perceived by listeners, and uses this information to form the basis of s wind noise detector / meter for analyzing audio files you can access the Journal here:

Or if you don’t have access, the paper is will also be available here (the next couple of days)

http://usir.salford.ac.uk/id/eprint/32802

If you want to run the wind noise detection algorithm you can do so using the code here

https://github.com/kenders2000/WindNoiseDetection/

Wind noise detection open source program

6 May 2014

We have a developed an algorithm which is able to measure the level of wind noise on your recordings. This algorithm is the result of research carried out for our project where we carried out perceptual studies about the effect of microphone wind noise on sound quality of recordings. We then developed an algorithm which was able to analyse audio files and detect wind noise and predict the level of degradation to audio quality.

This program is useful to people who may have a lot of audio files they want to quickly sort through to find versions of recordings without wind noise. Or if they want to quickly located regions in recordings which are free of problems. A possible application of this technology is to collect together many recordings of an out door concert and without having to listen to all recordings piece together the best quality files.

The program has been uploaded to GIThub, it is a command line program written in c/c++ and needs to be compiled first.

http://github.com/kenders2000/WindNoiseDetection/

from here you can download and compile the program to use in your own applications.

We would love to hear how you get along using the program and what you have been using it for. We will be expand the program to detect other common recording errors to

The Good Recorder iPhone app

14 February 2014

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. Read more…..

Wind noise recordings – Validating a Wind Noise Detector

19 November 2013
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.