The most common type of broadband noise reduction works by splitting the audio into a large number of frequency bands. Each of these is then fed through an expander circuit, allowing signals falling below a certain level to be muted or attenuated. It would be impractical to set hundreds of separate thresholds manually, so instead a noise 'fingerprint' or profile is taken by analysing a short section of audio containing only noise, usually from before the start of a piece of audio or during a pause. The amplitude of the noise within each frequency band is measured, and this is used to set the threshold value for that band.
If no noise‑only section is available, it is possible to use a generic noise-spectrum profile based on, for example, tape hiss or analogue circuit hiss, adjusting the overall threshold manually until the best results are achieved. There's usually a facility to audition just the sound being removed as you adjust the overall threshold, so you can hear when parts of the wanted signal are being affected.
This was how the original SoundSoap worked.The next stage of refinement is to allow the software to analyse a section of audio and to use an algorithm to try to estimate what is wanted signal and what is noise. Real audio is constantly changing but noise tends to be either reasonably constant or slow‑changing, so by taking a long enough sample, the algorithm can make a reasonable prediction as to what is noise and what is wanted audio. The capture time will vary depending on the material, so in some systems the software decides how much audio it needs to 'see' to get a reliable result but gives the operator the option to change this if necessary. The noise data is then used to create a noise profile based on the average noise amplitude and spectrum over the sampling period. The clear benefit of this method is that you can work on audio that has been trimmed to remove any noise‑only sections. It can also be helpful on occasions to choose a section of audio where the noise is most exposed when creating a noise profile. However, it is only really effective when the noise character is fairly consistent.The Holy Grail is an algorithm that can figure out what is noise, even when the noise character is changing, and then adjust the expander thresholds to follow the changing noise profile in real time.
For this to work effectively, the noise character still needs to change more slowly than the audio, otherwise the software has no way to tell what is wanted audio and what is noise, so rapid changes in noise character, such as might occur at an edit point, may be exposed for a short time before the software has time to respond. On SoundSoap Pro, you can adjust how quickly the algorithm follows changes in the noise spectrum, but the compromise is that the faster you set the response time, the shorter the period of analysis and, as a consequence, the algorithm will be less precise in discriminating between noise and wanted signal. In theory, the more rapidly the wanted signal is changing, either in frequency content or amplitude, the faster the noise tracking can operate without tripping up.In all cases, manual adjustment of the overall threshold level may be required to get the best balance between noise removal and processing artifacts, and this is best done by auditioning the removed part of the signal. It is also important to attenuate the noise only as much as is necessary to avoid noticeable processing artifacts such as loss of treble or a watery, 'chirping' character. In some cases, making two passes over the same audio with a relatively low level of attenuation will produce more natural‑sounding results than a single pass with twice the level of attenuation. Vray sketchup mac serial.
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