Information only counts when a signal is actually transmitted

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: Information content of a signal is measured by its probability of occurrence; A source sending one symbol repeatedly conveys zero information despite infinite potential self-information.; Extreme acoustic congestion can erase the ability to distinguish any signal — Schafer, Murray R, The Soundscape- Our Sonic Environment…, 245 (+2 more)

Related

• Tiny periodic signals can be recovered from heavy noise via autocorrelation (similarity 0.81)
• Audio symbols with high probability carry less information and need fewer bits (similarity 0.80)
• A finite-length burst yields a short-lived autocorrelation that vanishes beyond its duration (similarity 0.79)
• Information content relates to the number of bits required to transmit it (similarity 0.79)
• Detection can be modeled as cross-correlation of the signal template with actual activity (similarity 0.79)
• Information content of a signal is measured by its probability of occurrence (similarity 0.78)
• Extreme acoustic congestion can erase the ability to distinguish any signal (similarity 0.78)

Broader Connections

• A source sending one symbol repeatedly conveys zero information despite infinite potential self-information. #exemplifies (0.85)
• Information content of a signal is measured by its probability of occurrence #exemplifies (0.82)
• Tiny periodic signals can be recovered from heavy noise via autocorrelation #exemplifies (0.75) — Eric J. Heller, Why You Hear What You Hear- An…, 432
• Discretizing a signal into levels is inherently lossy and produces error #complicates (0.75)
• Extreme acoustic congestion can erase the ability to distinguish any signal #complicates (0.72) — Schafer, Murray R, The Soundscape- Our Sonic Environment…, 245