|This is a page for technical questions about the algorithms used in Audacity's programming code. An algorithm can be defined as a finite list of instructions for accomplishing a task that, given an initial state, will terminate in a defined end-state.
If you have questions about how Audacity works, please post them here and the developers will answer them!
Eventually we will get a lot more organised and have explanations of the Audacity algorithms in format, where they can be both in the source code for Audacity and on a web page like here.
- ArchitecturalDesign describes the structure of the Audacity system architecture.
- AudacityLibraries describes the components that are combined together to make Audacity.
Not yet answered
- I know that Audacity has a 32 bit sample resolution, and that when mixed down to normal 16 bit wav, it renders much of the following moot...
As a wav file is nothing more than data points representing an analog waveform...
When gain/amplification (either negative or positive) is applied to this, the resulting interpolation by definition results in a less accurate representation of the original waveform.
I'm wondering if when running down the EDL (edit decision list), Audacity performs each gain change calculation separately, or if it's smart enough to look at all the gain adjustments in total, and interpolating only once, thereby reducing the accumulation of error.
For example, if I apply a negative gain of 1.7dB, then apply a positive gain of 3.0dB, then apply a negative gain of 1.3dB -- do I end up with the exact sample data points as when I started?
Are there any differences in using the gain slider, or the amplification technique to change the amplitude on a track? (primarily considering this possible lossy aspect of repeated interpolation)
About the algorithm
Q: How do you actually remove noise? What is the algorithm?
A: The noise removal algorithm uses : it finds the spectrum of pure tones that make up the background noise in the quiet sound segment that you selected - that's called the "frequency spectrum" of the sound. That forms a fingerprint of the static background noise in your sound file. When you remove noise from the music as a whole, the algorithm finds the frequency spectrum of each short segment of sound in your music. Any pure tones that aren't sufficiently louder than the fingerprint (above the threshold to be preserved) are greatly reduced in volume. That way, (say) a guitar note or an overtone of the singer's voice are preserved, but hiss, hum, and other steady noises can be minimized. The general technique is called .
The first pass of noise removal is done over just noise. For each windowed sample of the sound, we take a Fast Fourier Transform (FFT) and then statistics are tabulated for each frequency band - specifically the maximum level achieved by at least <n> sampling windows in a row, for various values of <n>.
During the noise removal phase, we start by setting a gain control for each frequency band such that if the sound has exceeded the previously-determined threshold, the gain is set to 0, otherwise the gain is set lower (e.g. -18 dB), to suppress the noise. Then frequency-smoothing is applied so that a single frequency is never suppressed or boosted in isolation, followed by time-smoothing so that the gain for each frequency band moves slowly. Lookahead is employed; this effect is not designed for real-time but if it were, there would be a significant delay. The gain controls are applied to the complex FFT of the signal, and then the inverse FFT is applied, followed by a Hanning window; the output signal is then pieced together using overlap/add of half the window size.
Q: How many frequency bands does the noise gate use?
A: In Audacity 1.3.3 + later we use an FFT size of 2048, which results in 1024 frequency bands.
Q: What causes the 'tinkling' artefacts, and what steps can and have been taken to remove them?
A: The tinkly artifacts happen when individual pure tones are near the threshold to be preserved -- they are small pieces of the background soundscape that survived the thresholding, perhaps because the background noise is slightly different from the fingerprint or because the main sound has overtones that are imperceptible but that boost them slightly over the threshold.
So while the Audacity noise gating algorithm could perhaps be improved, any Fourier-based noise removal algorithm will have some artifacts like the "tinkle-bells". They are a symptom of the problem of discrimination - deciding whether a particular analogue signal is above or below a decision threshold - that is central to the fields of digital data processing and information theory. In general the tinkle-bell artifacts are quieter than the original noise. The real question is whether they are more noticeable than the original noise. (For example, noise-gating the Beatles' Sun King track off the Abbey Road album is a bad idea, because the soft brushed cymbal sounds merge smoothly into the tape hiss on the original master recording, so tinkle bells and a related problem -- fluttering -- are prominent in noise-gated versions of that track.)
You can reduce the effect of tinkle bells by noise gating sounds that are well separated (either in volume or frequency spectrum) from the background noise, or by mixing a small amount of the original noisy track back into the noise gated sound. Then the muted background noise tends to mask the tinkle bells. That technique works well for (e.g.) noisy microcassette recordings, where the noise floor might only be 20 dB below the loudest sounds on the tape. You can get about 10dB of noise reduction that way, without excessive tinkly artifacts.
Q: I'd like to know which resampling algorithm Audacity uses. I`m studying resampling for my thesis and I`m testing the influence of Audacity's resampler on perceived audio quality.
A: Audacity uses the resampling algorithm from Julius Orion Smith's Resample project. Audacity also contains code to use libsamplerate, but we can't distribute libsamplerate with Audacity because of licensing issues.
For more information on our choice of resampling algorithms: