Detection can be modeled as cross-correlation of the signal template with actual activity
Evidence
This works so well that tiny signals can be rescued from a lot of noise, as seen in figure 4.13. A pure noise signal and its autocorrelation are shown at the bottom. A noisy signal with a smooth periodic signal embedded appears at the top. The combined signal looks as ragged as the one at the bottom, which is pure noise. However, computing the autocorrelation reveals the hidden signal (top right). — Eric J. Heller, Why You Hear What You Hear- An…, 432 (
#exemplifies, via linked claim)
Categorization
- XREF: connects to: Narrow frequency bands allow removal of correlation and silence flagging; Autocorrelation discriminates echoes from genuine distinct sounds.; Tiny periodic signals can be recovered from heavy noise via autocorrelation — Eric J. Heller, Why You Hear What You Hear- An…, 432
Related
- Tiny periodic signals can be recovered from heavy noise via autocorrelation (similarity 0.89)
- Autocorrelation discriminates echoes from genuine distinct sounds. (similarity 0.86)
- Autocorrelation measures periodic self-similarity in a signal across time delays. (similarity 0.82)
- Detection is modeled as cross-correlating a stored supra-threshold template with the actual activity pattern. (similarity 0.82)
- A finite-length burst yields a short-lived autocorrelation that vanishes beyond its duration (similarity 0.81)
- Correlograms track spike-train periodicity over time via sliding windows (similarity 0.80)
- Autocorrelation reveals a signal’s fundamental periodicity through periodic peaks. (similarity 0.79)
- Information only counts when a signal is actually transmitted (similarity 0.79)
- The auditory system can be modeled as computing autocorrelation functions per frequency band (similarity 0.78)
Broader Connections
- Autocorrelation discriminates echoes from genuine distinct sounds.
#exemplifies(0.85) - Narrow frequency bands allow removal of correlation and silence flagging
#complicates(0.75) - Tiny periodic signals can be recovered from heavy noise via autocorrelation
#exemplifies(0.72) — Eric J. Heller, Why You Hear What You Hear- An…, 432