Could you clarify the difference between floating- and fixed-point 32-bit operation in the digital domain. I know that floating-point systems allow for data to be handled at word lengths above 24-bit, which are then dithered back down. Does it also result in a greater dynamic range?
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Technical Editor Hugh Robjohns replies: Accurate digital audio capture and reproduction requires, at the very most, 24-bit resolution. The reasoning behind this is that a 24-bit signal has a theoretical dynamic range of 144dB, which is greater than the dynamic range of the human ear, so, in theory, a 24-bit system can record sounds slightly quieter than those we can hear and reproduce sounds louder than we can stand. There is therefore no need for A-D/D-A converters to work at resolutions higher than 24-bit.
Fixed-point systems use the 32 bits in the conventional way to provide an internal dynamic range of about 192dB. Systems that use fixed-point 32-bit processing (like the 0-series Yamaha desks) usually arrange for the original 24-bit audio signal to sit close to the top of that 32-bit processing number to provide a lower noise floor and slightly greater headroom for the signal processing. (Incidentally, a 192dB SPL is roughly equivalent to two atmospheres' pressure on the compression of the wave and a complete vacuum on the rarefaction.)
Floating-point systems also use 32-bit numbers, but organise them differently. Essentially, they keep the audio signal in 24-bit resolution, but use the remaining bits to denote a scaling factor. In other words the 24-bit resolution can be cranked up or down within a colossal internal dynamic range so that, in effect, you can never run out of headroom or fall into the noise floor — there is something like 1500dB of dynamic range within the processing, if the maths is done properly.
Most high-end consoles and workstations employ floating-point maths because (if properly implemented) you can get better performance and quality in the computations. Most budget/low-end consoles and DAWs use fixed-point processing because it's easier and faster, and can be implemented in hardware more easily.
Published January 2004
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