Digression: common characteristics in signal analyses

Time signals, waveforms and oscilloscope analyses

 ‐introduction with examples

Envelopes and transients. VU metering.

 ‐easy to see ⇒ first to be shown
 ‐VU an extension
 ‐VU: squaring/energy
 ‐not ’what is seen’
 ‐remember to discuss analysis scale

Periodicity/quasi‐periodicity. Noise.

 ‐periodicity easy to see
 ‐quasi‐periodicity when no noise
 ‐noise by example
 ‐remember: noise and high frequencies difficult to discern

Discontinuities/clicks/glitches, hum and distortion

 ‐low frequency periodicity with no envelopes
 ‐signs of digital and/or analog clipping
 ‐’rough edges’

High/low frequencies.

 ‐difficult to discriminate
 ‐esp. from noise and in transients

Spectrum analysis

Long term analyses and spectral accuracy

 ‐the loss of information into the unreadable phase
 ‐misleading results from glitches (time localized data overall)

Windowed Fourier transformation. Windowing effects.

 ‐phase not so relevant
 ‐importance of proper choice of scale
 ‐spectral blurring/’spilling between bins’
 ‐bin respose transition shapes may be distorted with some windows: tradeoff for rapid transitions

Multiresolution analysis: wavelets and subbands

 ‐spread of information across frequency/timescales
 ‐generation of data (realistic transients tend to excite the basis wavelet itself into the analyses)