> Probably my short remarks are not helpful for anyone ... there are a number
> of audiophiles on the list and I thought they would see similarities
> removing sound distortions ... somewhat similar thought process as is done
> digitally with images.
There really are similarities. The entire concept of
expose-to-the-right, is based on the same concept of maximizing
signal-to-noise in audio. Crank the input to as close to the clip
point as possible without clipping. Then during the mixdown you pull
the volume down to the proper level. Inotherwords, always record
hotter so you pull the levels down, never up.
Other similarities involve high-pass and low-pass filtering. Curves
adjustment is just like using audio compression or expansion.
Noise filtering in audio is an interesting beast, though. The general
concept is to sample the noise pattern and then apply a subtractive
algorithm to the sound to remove the noise pattern. Some algorithms do
this better than others, and most require a multi-pass approach to do
well.
There are some interesting studies that show the direct coorelation
between colors and notes. By applying audio spectrum to visible
spectrum, you get clashing colors in the same manner than notes will
clash. I started looking at that in 1994 when a researcher for some
university bought some equipment from me to test it.
Back to noise filtering. Pattern noise in audio and image data is very
easy to address. But the problem with both is that they cannot
efficiently change with the changing image/sound scape. For example,
the sound of an idling car can be removed. But at the point where the
driver puts the car in gear and accelerates, the sound would reappear
since the signature has changed. This is why most noise removal
algorithms look at the overall dynamic range of the noise, get some
basic pattern infromation and then apply a broad brush approach to the
removal. Another highly successful method is to identify what it is
that you want to keep. By identifying the "real" sound, you are able
to subtract out what it is that doesn't look or sound like the real
sound.
Unfortunately, in the case of audio noise reduction, there has been
very little significant development in that area in over 15 years. The
algorithms have been improved a bit, but we're pretty much stagnent in
that subject. In fact, we've actually seen the reduction in tools
since a couple companies that had effective NR systems are no longer
in existance.
AG
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