Signal

General

See also Electrical, Audio, Video, Computing, Creative coding, Media, etc.

• https://en.wikipedia.org/wiki/Signal_(electrical_engineering)

• https://en.wikipedia.org/wiki/Signal_processing

• https://en.wikipedia.org/wiki/Nyquist–Shannon_sampling_theorem - a fundamental bridge between continuous-time signals (often called "analog signals") and discrete-time signals (often called "digital signals"). It establishes a sufficient condition for a sample rate that permits a discrete sequence of samples to capture all the information from a continuous-time signal of finite bandwidth.

• YouTube: Digital Signal Processing Basics and Nyquist Sampling Theorem

• YouTube: Circuits and Systems (By Prof. Ali Hajimiri) - playlist

Anti-aliasing

• https://en.wikipedia.org/wiki/Aliasing - an effect that causes different signals to become indistinguishable (or aliases of one another) when sampled. It also often refers to the distortion or artifact that results when a signal reconstructed from samples is different from the original continuous signal.Aliasing can occur in signals sampled in time, for instance digital audio, and is referred to as temporal aliasing. Aliasing is generally avoided by applying low pass filters or anti-aliasing filters (AAF) to the input signal before sampling and when converting a signal from a higher to a lower sampling rate. Suitable reconstruction filtering should then be used when restoring the sampled signal to the continuous domain or converting a signal from a lower to a higher sampling rate. For spatial anti-aliasing, the types of anti-aliasing include fast sample anti-aliasing (FSAA), multisample anti-aliasing, and supersampling.

• https://en.wikipedia.org/wiki/Anti-aliasing_filter - a filter used before a signal sampler to restrict the bandwidth of a signal to approximately or completely satisfy the Nyquist–Shannon sampling theorem over the band of interest. Since the theorem states that unambiguous reconstruction of the signal from its samples is possible when the power of frequencies above the Nyquist frequency is zero, a real anti-aliasing filter trades off between bandwidth and aliasing. A realizable anti-aliasing filter will typically either permit some aliasing to occur or else attenuate some in-band frequencies close to the Nyquist limit. For this reason, many practical systems sample higher than would be theoretically required by a perfect AAF in order to ensure that all frequencies of interest can be reconstructed, a practice called oversampling.

• Polynomial Transition Regions (PTR) - “Reducing Aliasing from Synthetic Audio Signals using Polynomial Transition Regions,”to appear in IEEE Signal Processing Letters.

• IMPROVED POLYNOMIAL TRANSITION REGIONS ALGORITHM FOR ALIAS-SUPPRESSED SIGNAL SYNTHESIS | Semantic Scholar - One of the building blocks of virtual analog synthesizers is the oscillator algorithm producing simple geometric waveforms, such as saw or triangle. An important requirement for such a digital oscillator is that its spectrum is similar to that of the analog waveform, that is, the heavy aliasing that would result from a trivial modulo-counter based implementation is reduced. Until now, the computationally most efficient oscillator algorithm with reduced aliasing was the Polynomial Transition Regions (PTR) method. This paper shows that the efficiency can be increased even further by eliminating the phase offset of the PTR method. The new Efficient PTR (EPTR) algorithm produces the same output as the PTR method, while requires roughly 30% less operations, making it the most efficient alias-reduced oscillator algorithm up to date. In addition to presenting an EPTR sawtooth algorithm, the paper extends the differentiated parabolic wave (DPW) triangle algorithm to the case of asymmetric triangle waves, followed by an EPTR implementation. The new algorithm provides continuous transition between triangle and sawtooth signals, while still requires low computational power. LESS

ToSort

• https://www.minidsp.com

• https://sourceforge.net/projects/rephase/

• https://github.com/ZipCPU/dspfilters - a variety of demonstration filters. These filters will be discussed and used as examples on the ZipCPU blog at zipcpu.com. If you watch carefully, you may find filters here before they are posted, as I'm going to be doing my development here. Still, there have been many posts already that you may find valuable. These include: A description (and implementation of) the two simplest filters I know of; A Generic FIR implementation; A Simpler Generic FIR implementation; A Moving Average/Boxcar Filter; A Linear Feedback Shift Register (LFSR); Building a generic filtering test harness; Measuring a filter's frequency response; Delaying elements in a DSP system; Generating a Pseudorandom noise stream via an LFSR; An Example LFSR Output; And How to generate multiple bits per clock using an LFSR; Testing a generic filter using the test harness; Building a slower filter, one that time-multiplexes a single one hardware multiply across many coefficients.