‘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,
and the two spectra.
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.