Very low bit-rate audio coding requires working in the frequency domain

Evidence

Representing signals in the frequency domain, as decompositions into their frequency components, is important not only as a powerful signal processing technique but also because it resembles the way the human ear analyses signals. Audio signals that we are able to hear as sounds are thus often analysed by means of frequency transforms in order to find their audible features and cues. — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 377 (#corroborates, via linked claim)

Categorization

• XREF: connects to: Programs are compressed for noisy portable listening environments; The 1930s vocoder pioneered reducing speech to slowly-varying parameters to cut bandwidth — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 2225; Perceptual coders exploit masking to cut audio storage and bandwidth — David Howard, Jamie Angus, coustics and Psychoacoustics, 1656 (+2 more)

Related

• Frequency-domain coding using masking avoids transmitting masked sound information (similarity 0.86)
• Perceptual coders exploit masking to cut audio storage and bandwidth (similarity 0.86)
• The 1930s vocoder pioneered reducing speech to slowly-varying parameters to cut bandwidth (similarity 0.84)
• Audio compression exploits masking to discard inaudible signal parts (similarity 0.83)
• Frequency-domain analysis mirrors how the human ear processes sound (similarity 0.82)
• Masked audio components below the masking threshold need no bits transmitted (similarity 0.82)
• Frequency-domain processing enables efficient audio coding for monophonic and multichannel signals (similarity 0.82)
• Narrower frequency bands make audio signals less correlated and easier to quantize (similarity 0.81)
• Audio coding aims to reconstruct signals while other processing modifies them (similarity 0.81)
• Audio coding systems compress signals to fit limited channels and storage (similarity 0.81)
• Audio compression maximizes information by encoding only unpredictable signal parts (similarity 0.81)
• DFT-based windowing explains basic auditory frequency resolution properties (similarity 0.81)
• The ear’s analysis resembles Fourier frequency decomposition of sound (similarity 0.81)
• Choice of audio coding domain depends on the application (similarity 0.80)
• Time–frequency audio processing exploits the ear’s own spectral analysis structure (similarity 0.80)

Broader Connections

• Masked audio components below the masking threshold need no bits transmitted #expands (0.80)
• Frequency-domain analysis mirrors how the human ear processes sound #corroborates (0.72) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 377