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)
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 →
In an earlier blog we wrote about a Web experiment where we asked participants to compare and rate sounds tagged with “bird song” (or “birdsong”) on Freesound.org. We then compared the quality ratings we had obtained with the Freesound metadata for each sample (such as average rating, how many downloads, etc). We found that 33% of the variance in quality ratings could be explained by the number of downloads per day of the sounds. An interesting finding – it hinted towards a rough-and-ready method for quickly sorting sets of audio into good and poor audio quality. Continue reading →
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
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
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.
In a recent blog post I discussed the possibility of using a proxy measure for quality. Rather than ask about quality directly, could we find another metric in (ideally free and accessible) existing data about user behaviour that we could rely on to predict quality?
Audio quality research often involves manipulating a known facet of a recording (such as distortion level, bit rate, and so on) and seeing what effect it has on people’s ratings of quality. Unfortunately however, the simple act of requesting a rating of quality can change the way people would normally listen to the recording. Recently we’ve been considering alternative ways of approaching this problem.
If, for instance, we could find another measure that predicted quality reasonably enough we might not have to ask directly for people’s ratings. And if this implicit measure of quality could be found quickly and freely, in data that already exists, we might have any number of new and exciting avenues to pursue.
In an earlier blog post we presented some findings from our web survey on the differences between iPhones and other brands of mobile phone. In this post we look beyond mobiles and give a brief overview of some of the other findings from the survey. Continue reading →
Early on in the project we put a survey on the web to ask questions about where and how people make audio recordings, and what they make recordings of. We also wanted to know what issues people reported as having the biggest impact on audio quality in their recordings (you can still take part in the survey by clicking here, it only takes a couple of minutes). Three months on, over 150 people have taken part and we have begun to analyse the data. One of many interesting trends to emerge is a series of differences between iPhones and other brands of mobile. 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 →
Hello and welcome to the project page for the university of Salford project, ‘The Good Recording Project’. Over the next three years, a team of researchers at Salford, Acoustic Research Centre, will be investigating the effect that common recording mistakes have the perception of audio quality in recordings. We are particularly interested in user generated content. YouTube is a huge phenomenon where many hours of footage are uploaded every minute. However the quality of footage varies greatly. Professional generators of content are not immune to these recording mistakes either. Therefore we plan to investigate the effect that these mistakes have on the perception of the audio quality. Continue reading →