‘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.
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
The wind noise was simulated based on a number of realistic models. This allowed us to generate a huge range of possible examples. The scheme adopted was a supervised learning one. This is where a set of audio features are extracted and a target value (the wind nosie level) is associated with this feature vector. a large number of examples are generated an classified according to wind noise level. A support vector machine is then train to try to classify between two groups, where one group contains features from wind noise above a certain level and the other below. A support vector machine (SVM) is a binary classifier where the objective find a line, (or a plane or hyper plane depending on the number of dimensions of the features) which can be drawn in the feature space which will separate between the two groups. A number of SVMs are trained using different wind noise levels as a thresholds. Three thresholds are chosen so that four class are defined: high, medium low and undetectable. Three SVMs are trained and the data combined using a decision tree. The results are very promising which simulated data showing detection rates of 87%, and real world test also showing good promise.
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.
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 →