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)
If you want to run the wind noise detection algorithm you can do so using the code here
One aspect of the Good Recording project is to develop algorithms which will be able ‘listen’ to audio and make judgement on the quality. I thought it would be interesting to have a look into the history of machines which can listen and act upon audio. This is application area is known as machine audition . The most well known modern algorithm is that of apple’s speech recognition personality Siri. But there are other aspect of our lives where machine audition is carried out. Think of the song identification applications Shazaan and Soundhound. These applications are great for identifying a song you just heard on the radio. These devices and algorithms are sound identifiers or classifiers where a sound is recorded and then classified, perhaps identified as a particular piece of music or a particular word, or classified as being a particular style of music or language. Continue reading
So for the past few months have been have been investigating microphone wind noise. We choose microphone wind noise as this came very high in our online survey into the main issues that can degrade the audio quality. The survey is ongoing so do please take the time to carry it out if you are interested.
To investigate microphone wind noise, the first task is to understand how it is generated. Luckily has already been significant research that has already been carried out to this aim, so a thorough literature review was carried out. The dominant source of wind noise in outdoor microphones are turbulent velocity fluctuations in the wind which interact with the microphone and are converted to pressure fluctuations. There are other less significant factors which can contribute, for example when the microphone is embedded in a device and this is placed in a flow, this can cause vortex shedding and other resonant type behaviors. Continue reading