Sound spectrum analysis depends on banks of filters to decompose signals

Evidence

Because the spectrum of a sound is an important part of the way we perceive it, there is often a need to look at the spectrum of a real signal. The way this is achieved is to use a bank of filters, as shown in Figure 1.55. — David M. Howard, Jamie Angus, Acoustics and Psychoacoustics, 464

Categorization

Category: Causal Relationships

Related

• Filtering in the frequency domain is simply multiplicative on spectra (similarity 0.85)
• Filtering a signal distorts its time response as well as its spectrum (similarity 0.84)
• Filters and the signal’s time response are physically coupled tradeoffs (similarity 0.84)
• The hearing system analyzes complex sounds by linear superposition of component responses (similarity 0.83)
• Instrument sound equals input spectrum multiplied by body response (similarity 0.83)
• Filter banks split broadband signals into sub-bands much like cochlear hearing does (similarity 0.82)
• Filter banks drive equalization, audio coding, and spatial reproduction (similarity 0.82)
• Broad filters and lost suppression increase masking and blur spectral contrasts. (similarity 0.81)
• An instrument’s output spectrum equals its input spectrum weighted by the body’s resonance response (similarity 0.81)
• Auditory filters are analyzed one component at a time to build up complex effects (similarity 0.81)
• The auditory spectrum differs from the physical power spectrum in resolution and shape (similarity 0.81)
• Higher-frequency auditory filters carry more intensity-difference information than the signal-centered filter (similarity 0.81)
• Narrower frequency bands make audio signals less correlated and easier to quantize (similarity 0.81)
• Keeping total level constant while widening bandwidth requires lowering spectral density (similarity 0.81)
• Filtering operates as multiplication in the frequency domain, a key spectral analysis technique (similarity 0.81)