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60 lines
2.9 KiB
60 lines
2.9 KiB
8 years ago
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#summary Computing sines with polynomials
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= Sine generation: polynomials =
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There are two techniques used in the synthesizer for generating sine
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waves. The scalar code uses a 1024-element lookup table with linear
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interpolation, but the NEON code uses a polynomial. Polynomial evaluation
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parallelizes easily, unlike lookups.
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Most math library implementations of sin() give results accurate to a
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couple of LSB's of floating point accuracy. But that's overkill for our
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purposes. We want a sine so that the errors are just below what you can
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hear. A sixth order polynomial is a good choice here, as the loudest
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harmonic (the 3rd) is almost 100dB down from the fundamental.
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Also, harmonic distortion in low frequencies is "musical". It seems silly
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to go to a huge amount of trouble to suppress harmonics and create a
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pure sine tone, when in practice these sines are going to be assembled
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into an FM modulation graph to make rich harmonics. However, high
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frequency noise is bad because it will create aliasing.
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The usual criterion for designing a polynomial for function approximation
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is to minimize the worst case error. But for this application, not all
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error is created equal. We're willing to tolerate a small increase in
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absolute error if we can shape the spectrum to concentrate the error
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mostly in the low frequencies.
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The design we ended up with was to compute a minimum absolute error
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for a 5-th order polynomial, then integrate it. The result is:
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{{{
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y = 1 - 1.2333439964934032 * x**2 + 0.25215252666796095 * x**4 - 0.01880853017455781 * x**6
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}}}
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In this graph of the error compared to true sine, you can see the difference.
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The absolute error computed with Chebyshev fitting is smaller, but there's
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a discontinuity when the sign flips, and the frequency gets high. The
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"smooth" variant gets rid of the discontinuity, and the high frequency
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ripples get attenuated, of course at the cost of the absolute error being
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higher.
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http://wiki.music-synthesizer-for-android.googlecode.com/git/img/cheby_vs_smooth.png
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The spectrum tells a similar story: the tail of the "smooth" variant has
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about 10dB less energy than the Chebyshev fit, while the low frequency harmonics
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are a touch higher.
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http://wiki.music-synthesizer-for-android.googlecode.com/git/img/cheby_vs_smooth_fr.png
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This was fun to to, as it felt like an optimization across all levels of
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the stack, down to cycle counts on the NEON code, and all the way up to
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how musical the tones would sound.
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= References =
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* [http://www.rossbencina.com/code/sinusoids Fun with Sinusoids] by Ross Bencina
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* [http://www.excamera.com/sphinx/article-chebyshev.html Chebyshev approximation in Python] — the simple but effective tool I used to compute the polynomials
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* [http://pulsar.webshaker.net/ccc/sample-506402de Pulsar cycle counter] with scheduling analysis of resulting NEON code for the inner loop (each iteration computes 12 values of the FM synthesis kernel, of which the bulk of the calculation is the sine)
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