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.
One of the major issues that was raised from our survey is when a device gets overloaded when presented with excessive sound levels. A common issue is recording the audio at a rock concert where the device is simply unable to cope with the sound pressure levels it is exposed to. In order to understand how devices respond when placed in this situation an experiment was designed to attempt to capture the kind of non-linear behaviours that may occur.
The full report is accessible here:
The performance of a series of common devices was quantified including the; Cannon 550D, Edirol r44, Neumann U87ai via Focusrite 2i4, Shure SM57 via Focusrite 2i4, Zoom H2, Zoom H4, Google Nexus 4, Apple Iphone and a Sony camcorder (vx2000).
Most devices have some form of dynamic gain control to prevent signal clipping, but the implementations clearly differ considerably. Some devices have many settings for different situations indicting that there is no one particular method suitable for all cases. The attack and release times of the measured systems range from 5 to 17 ms and 30 and 400 ms respectively. Some devices may also demonstrate a nonlinear gain curve with no attack or release but which try to limit audible distortion by using a compression ratio of between 1.4 and 10. While other systems have no protection and when presented with excessive sound levels will exhibit hard clipping.
We are interested in how people perceive the quality of user-generated content and to help us understand this better we are currently carrying out an experiment comparing youtube clips of glastonbury. If you would like to take part please click here, its quite interesting how different devices and positions in the audience can make such a big difference to the sound.
Also from a sound engineering perspective providing a good quality sound to the whole audience is a very difficult task, you need to be part engineer part meteorologist, as the weather can have such a huge effect on the sound, read prof. Cox’s blog for more info.
Our project is focusing on how to improve the quality of recordings on mobile consumer devices. This article by the BBC new team suggests the reason why many artists are against the recording of concerts on smart phones is because of the lower level of quality.
In my opinion I think it would be interesting to see if artist’s opinons change when the quality of recordings increases. I think it could be likely that the real issue for artists is a loss of control of their art form. I would be interested to see what other people thought.
So we have we have been working on a number of things recently. We have finished our web experiments where we have been looking at the influence of wind noise on the perceptual quality of speech. For this experiment people were asked to listen to samples of recordings with added wind noise and rate the quality, attempt to repeated what was said and rate the difficulty of the task. We varied the wind noise sample in term of level and ‘gustiness’. We are analyzing the data at the moment attempting to understand how level and gustiness relate to sound quality for this particular case.
Wind noise Detector
In addition to these subjective tests we have developed a ‘wind noise detector’. This algorithm listens to an audio stream and detects the presence of ‘wind-noise’. The detector compresses the information within the audio stream by extracting ‘audio features’. Audio Features are efficient representations of sounds. The amount of data required to represent an uncompressed digital audio stream is very large and to build a detector which utilized the raw audio stream is simply not possible. Therefore features must be extracted which can represent the information present in the stream much more efficiently Luckily, by an understanding of how sound is processed by the human auditory system, gives us a way of compressing the information stream, throwing away all the perceptually unimportant parts while keep the salient features. This is the how mp3 and other compression method achieve their high compression ratios. See the later topic for more information on the features extraction.
Teaching a machine to detect wind noise
Audio Features – Mel-Frequency Cepstrum coefficients
The audio features representation called Mel Frequency Cepstrum Coefficients (MFCC) is commonly used in speech recognition to compress the information stream prior to the recognition stage. The MFCC is a spectral representation of a signal over a (usually short eg 20 ms ) time period. A spectral representation means, rather than representing the signal in the time domain i.e. how the pressure fluctuations over time the representation simply shows the levels of the different frequency components with the analysis time period (this time window is often referred to as a window). The ‘Mel’ part refers to the frequencies over which the spectrum is evaluated. A Fourier transform has a linearly spaced frequency components, however this is not how the human auditory system performs The human system is sensitive over a logarithmic scale, in other words the change in frequency for a low pitched sound is much more noticeable compared with the same change but a t a higher pitch. The Mel scale attempts to represent how the human auditory system represents pitch.
Cepstrum – The cepstrum is a representation of a signal where the inverse Fourier transform of the log spectrum is computed. A property of the logarithm is that process that previously were multiplicative become additive, this enables components parts of signals to be separated more easily. For example speech spectra can be thought as a product between the spectra of the speech source and the vocal tract. The vocal tract produces resonances or ‘Formants’, by computing the cepstrum the formats and speech source components can be separated out, where low ‘quefrency‘ components represent the spectral envelop of formants and higher components represent the speech source.
Therefore the Mel-frequency cepstrum is a representation of the spectral envelope of a signal where the frequency scale is warped to be representative of the human auditory system. Typically this reduces the data in a 20 ms wind sampled at 44.1 kHz from 1102 samples to 12 MFCCs. This is a very efficient representation and much of the salient information is preserved.
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
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