Quantization noise drops by 6 dB per added bit while hearing spans 130 dB

Evidence

Quantization with finite precision generates an error called quantization noise. The signalto-noise ratio (SNR, see Section 4.2.6) describes the level of the signal compared to the level of noise, and for quantization noise it improves by 6 dB for each added bit, so that 16 bits, often used in audio, yield a maximum SNR of about 96 dB. Because the dynamic range of the auditory system is about 130 dB, even more than 22 bits may be needed. — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 408

Categorization

Category: Factual Claims

• DEFINE: Quantization noise is the finite-precision error introduced when an analogue signal is converted to discrete binary levels; SNR scales 6 dB per bit.
• ADDENDUM: Directly useful as technical grounding for any writeup on why auditory dynamic range outpaces standard audio bit depths.

Related

• Each added quantization bit yields 6dB SNR improvement (similarity 0.97)
• Higher bit depth reduces quantization noise by resolving more amplitude values (similarity 0.87)
• Quantization always injects noise that lowers audio signal-to-noise ratio (similarity 0.87)
• Discretizing a signal into levels is inherently lossy and produces error (similarity 0.85)
• Shaping quantization noise to sit 13 dB below the signal renders it inaudible (similarity 0.85)
• Uniform quantization noise requires about 60 dB signal-to-noise to stay masked (similarity 0.85)
• Quantizer levels need not be powers of two when entropy coding is used (similarity 0.84)
• Scale-factor precision trades quantizer utilization against side-information bit cost (similarity 0.82)
• Psychoacoustic noise shaping achieves an effective 120 dB SNR on CD audio (similarity 0.82)
• Sine wave inputs achieve 1.76 dB better quantization SNR than uniform signals (similarity 0.81)
• Quantization converts continuous signals to discrete levels and is lossy (similarity 0.81)
• Non-uniform quantization reduces audible noise by spacing levels by probability. (similarity 0.80)
• Logarithmic quantization suits speech but distorts music beyond tolerance (similarity 0.80)
• Nonuniform quantizers reduce perceived noise by spacing levels of rare louder signals farther apart (similarity 0.79)
• Loud-signal masking of weak signals enables bit-saving adaptive quantizers (similarity 0.78)