I am often asked to explain my work to those from a non-academic background, or who have little or no knowledge of acoustics. The answers I typically give are invariably either too woolly on account of my fatigue, too technical on account of my deep-rooted inferiority complex, or too dull on account of my inability to speak fluidly and enthusiastically when put on the spot. I have therefore decided to add this section to my website for interested parties to peruse at their leisure.
Why do I care whether or not the plebs understand my work? Well, because one of the ideas that motivates my PhD is my belief that people of all ages and backgrounds ought to be encouraged to engage more with the sound world. With sound being such a fundamental aspect of our emotional and physical well-being, I consider it an area in our lives that is sorely neglected. Environmental sound in particular, should be seen as a largely untapped source of pleasure and intrigue, rather than an unwelcome intrusion. I hope that my work, as well as gaining me my PhD (number 1 aim!), will ultimately prove interesting and accessible to a much broader audience than the fragment of academia it currently concerns.
An introduction to Auralisation (that word in the title of my PhD and all my publications thus far
This elusive term is increasingly finding its way into the public domain, yet without adequate explanation, such words only serve to frustrate and alienate the general public from work that should seek to engage and inspire them. Michael Vorlander writes,
“The expression ‘auralisation’ is analogous to the well-known technique of ‘visualisation’. In visual illustration of scenes, data or any other meaningful information, in movie animation and in computer graphics, we describe the process of ‘making visible’ as visualisation. In acoustics, auralisation occurs when acoustic effects, primary sound signals or means of sound reinforcement or sound transmission, are processed into an audible result. The word auralisation is used today to describe the process of signal generation, processing and reproduction as well as the result: the perceivable sound as auralisation of an acoustic problem, a room, a building, a car, or any other industrial product.”
Certainly for non-acousticians, this explanation fails to satisfy as it is difficult to see why auralisation should be ranked independently of other forms of audio synthesis. I think the main way that it differs and which should be highlighted, is the involvement of space; in short, auralisation concerns the audible rendering of sound in a space.
Thinking in terms of the visual analogy to auralisation in terms of Computer Generated Imagery (CGI), the animator seeks to place an object or actor in a 3D space. Just as an image alters according to the relationship between object and observer, so the characteristics of a sound depend, to a variable extent, on the position of the sound source(s) in relation to the listener. Using computer algorithms, the animator needs only to input a limited amount of information in order to recreate the same scene from different distances and viewing angles. Auralisation software attempts to do precisely this for sound in a space.
This is best explained using a real-world example. A live music venue wishes to predict how a performance will sound from different locations in the auditorium. This information will allow them to design their PA system accordingly. They may even go as far as to send different versions of the ‘mix’ to different speakers in the auditorium.
So how is this ‘auralisation’ performed?There are various techniques for performing auralisation. A common technique used in a lot of professional acoustic software for its speed and efficiency is known as Ray Tracing. Very simply, Ray tracing imagines the path of sound as rays fired off in multiple directions from a source. These rays are, to varying extents, reflected or absorbed by objects and surfaces in the space. Some of the rays will happen to cross paths with a receiver in a specific location. In this way, what is known as a histogram is built at the receiver by recording the passing rays and their magnitudes in time. The resulting histogram, which can be thought of as the ‘acoustic fingerprint’ of the space, is referred to in the scientific community as an ‘impulse response’: the response of a closed system (i.e. the room) to a burst of noise. Things do get a bit more complicated than that in practice, but the basic idea behind it is very simple.
Another technique, and one which I use in my own simulations, involves building a model of the system as a whole, and trying to simulate how sound waves propagate in a space. To do this, a volume of space, (or surface if one considers it a slice of zero height through a larger volume), is divided into discrete elements (i.e. chopped up into lots of little pieces of equal size). Each element represents a single point in the space. Casting your mind back to high-school physics, you may recall that audible sound is caused by the vibration of particles of air. Their movement causes tiny periodic fluctuations in air pressure which, within a certain range of frequencies, are detected by the mechanical parts of the ear and interpreted by the brain as ‘sound’. At each of the points in our computer model holds the value of the sound pressure. In the simplest of ‘wave-based’ models, we are aiming to emulate how sound propages from a point as if you had thrown a rock in a pond and wanted to observe the resulting ripples.
we are going to make the following assumptions about the behaviour of sound: Firstly, that sound is going to travel in all direction equally. Secondly, we assume that at any point in time, there is always the same amount of acoustic energy in the room – only the distribution changes. A relatively simple computer algorithm is all that is needed to update the pressure points in the space one by one at each point in time. An algorithm is simply a method consisting of a series of instructions, (often a single instruction repeated in a looping fashion), which leads to a predictable result. In this instance, all the algorithm is doing for each pressure point, is looking at the values of the point and its surrounding points in order to calculate the net energy that will remain after at the next point in time, having taken into account any losses and gains. The actual maths that is involved in the calculations is surprisingly simple for a case like this.
Ok, so now you should have a rough understanding of auralisation and acoustic simulation. If not, then I probably haven't done a very good job explaining it...so feel free to drop me an email with your suggestions on where I went wrong (either that or you are just hopelessly retarded). For now, let’s assume you got atleast 90% of it and move onto the specific area in acoustics that my research is concerned with: auralisation of sonic crystals for designing artistic noise interventions.
Sonic Crystals
Sonic crystals sound rather more mysterious than they are. In fact they are just a form of acoustic absorber based on their analogy with photonic crystals – manmade crystalline structures which scatter light in a similar way to natural crystals. The lattice structure of crystals that is responsible for their intense colouration or opalescence. What is essentially happening inside a crystal lattice is that waves are being bounced around inside a structure containing parallel surfaces. At certain frequencies, where some multiple of the wavelength or half wavelength matches the distance between the surfaces, there will be some cancellation or ‘summing’ of that particular frequency component. The end result, sticking with the example of natural crystals and light waves, is that certain frequencies of light are absorbed completely by the structure, while others are reflected with a greater intensity.
So, basically, what we’re dealing with here is a very selective kind of absorber. Now, in an environmental noise context, one could conceive of wishing to attenuate a particular range of frequencies whilst leaving others in tact, in order to filter or ‘shape’ the noise into something more interesting and more pleasant. For example, the sound of a river is widely considered relaxing, partly due to its association with nature, but also due to its frequency distribution. Conversely, a busy road is disliked by many for the same reasons. Perhaps, using a carefully designed ‘sonic crystal’ as a filter, it would be possible to make the road noise resemble the sound of a river. Now obviously there are other reasons why a river is preferred to road noise – for example, the temporal structure of the noise is a major factor - but frequency distribution is by no means a trivial aspect of the larger problem. Even if it were to be addressed in isolation, it might have a significant effect on how people perceive and react to a particular noisy environment.
To be continued...
Still to come
- What sounds do people prefer, how do we know that, and why?
- How might sonic crystals help?
- Let’s see some examples then...
