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;
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)
‘The Pips’ are series six of short tone bursts transmitted on Radio 4, they are known as the Greenwich time signal and are intended to accurately mark the start of the hour. They have been transmitted since 1924, and originate from an atomic clock.
On the 21st July 2014 a listener wrote to the Radio 4 programme ‘pm’ to ask why the pips had been changed. The programme played the offending pips and the originals. (here is a link to the program, the item is at 28m 31s http://www.bbc.co.uk/programmes/b049y9pn)
Here is an ‘old’ pip:
and a ‘new’ pip,
You may think that ‘new’ pip sound harsher, by looking at the wave form and spectra we can begin to understand what has happened. Here are the two waveforms of the pips,
Waveforms of the two pips
and the two spectra.
Frequency Spectra of the two pips
We can see from the spectra there are additional lines in the spectrum known as harmonics, comparing the two waveforms we can see that the ‘new’ pips appear to be similar to the older ones except that the peaks of the waveform have been flattened or ‘Clipped’ a little.
This clipping is a form of distortion, it occurs when the gain applied to the signal is to great or if there is a fault in a preamp and the amplifier is no longer able to properly replicate the signal at the input. We can clearly hear the difference between the two signals and according to the concerned listener (and his cat) it has a very negative impact on the sound quality. Denis Nolan, the network manager for radio 4, identified the fault as being due to a particular desk the signal was going through.
In our project we are writing an algorithm to perform a similar function to the upset listener, we don’t mean that our algorithm will write pithy letters to Eddie Mair, we want to build an algorithm to automatically detect when something like this has gone wrong and the sound is being distorted. The way we are going about this is to simulate all sorts of types of faults on many different types of sounds, and then see if we can look for ‘features’ of the audio which seem to be very dependant on theses faults. We can then build automated systems that look for occurrences of these features to locate them, and try and estimate how bad the error is from the features themselves.
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.
Handling noise occurs when a user brushes or knocks a recording device while recording is in progress. There are two types of handling noises, rubbing type noises, as may occur as a device is brushed against clothing and more impulsive types of noise such as when a mic is dropped.
Here are is an example of handling noise. The noises were recorded on an iphone and added to sounds taken from Freesound.
These type of sound can be highly disruptive, due to the proximity of the noise generating mechanism to the microphone diaphragm, these types of sounds can cause a significant reduction in signal to noise ratio. It is our plan to design and implement a series of detection algorithms which can identify when these type of sounds may occur, This can enable regions in sounds to be labeled automatically when this kind of problem occurs.
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 identifiedas a particular piece of music or a particular word, or classifiedas being a particular style of music or language. Continue reading →