FAQ

My music player has sound visualisation — are Sunics related to this?

No, not at all. Media players generate images which are influenced by the sound. Cyclograms are the sound — every pixel encodes a real data point from the original signal.

How does Sunics relate to the Fourier Transform?

Sunics does not involve spectral analysis or transformation into the frequency domain. It is an alternative way of modelling a signal. Roughly speaking, Fourier splits a signal into its overtones — while Sunics splits it into its timbre and pitch.

What type of signals can Sunics be applied to?

Any signal, time-series or data where the following holds:

• it allows for the definition of ‘cycles’ (see below)
• it consists of a relatively large number of cycles — hundreds or thousands

Put simply: any data representing a vibration, oscillation, rotation, pulsation, knocking or other repetitive process of some duration. Audio, heartbeats, rotating machinery, seismic signals, ground vibration.

What is the definition of ‘cycle’ used by Sunics?

The definition depends on the context of the signal. Generally, a cycle represents one of a succession of recurring signal segments, such as:

• a period of a sound wave
• a cardiac cycle
• a full rotation of a shaft
• a lunar month

For each example, a cycle is a unit of information where comparison and trend analysis matter.

What is the ‘pitch’ of a signal?

The pitch is the number of cycles occurring within a fixed time. Depending on context: the note of a sound, the pulse rate of a heart, or the rotation speed of a machine.

What is the ‘wave shape’ of a signal?

The wave shape is the geometric shape of the time-waveform over the duration of one cycle. It is expressed as a mathematical function over the interval [0,1] — independent of the original cycle length (see normalisation below).

A sine wave at 50 Hz and one at 100 Hz have the same wave shape. If you say ’eeeee’, you produce a sound with a wave shape you might call ’e’. If you say ’eeooo’, the wave shape evolves from ’e’ to ‘o’ over time.

Cyclograms show exactly this evolution. At a glance, you can tell whether the sound is a steady ’eeeee’, a shifting ’eeooo’, or something wild like ’eeowaahooa’.

Can the colouring of the images be changed?

Yes. The colouring is a visualisation choice that can be adjusted for the application. Relevant factors include the range and distribution of signal values, accessibility requirements, and personal preference.

Can the range and distribution of colours be changed?

Yes. For signals where small deviations from the baseline are important — ECG recordings, for example — more colours are allocated to the range around the baseline, highlighting even small changes as distinct visual patterns. For audio signals with a more evenly distributed range, an equal colour distribution works better.

What is the difference between Sunics and a ‘Waterfall’ diagram?

Waterfall diagrams split time-waveforms into fixed-duration segments. This segmentation often produces gaps or overlaps, causing data loss or replication. Multiple segments may be averaged, losing detail.

Sunics segments by cycle boundaries — the natural periods of the waveform. No data is lost, no averaging occurs, and short events (transients) are fully preserved. Waterfalls operate in time-time coordinates. Cyclograms operate in the time-phase domain.

What is ’normalisation’ in this context?

During the calculation of a cyclogram, the time intervals of all cycles are rescaled to the unit interval [0,1]. The original cycle durations are stored as the sunic’s pitch. With all cycles normalised to equal duration, the geometric shape of the waveform becomes independent of pitch — defining the wave shape.

Can the transformation be inverted?

Yes. The transformation between a signal and its sunic is lossless and reversible. Given the cyclogram and pitch, you can reconstruct the original time-series, sample-accurate.

Can Sunics be implemented on devices with limited capacity?

Yes. The core calculation is compact and has been prototyped on mobile devices. Computational requirements scale with signal complexity and update rate, not with arbitrary windowing parameters.