Autocorrelation predicts perceived pitch of non-harmonic partial groups

Evidence

The residue pitch formula estimates the time of maxima in the autocorrelation function, and therefore the corresponding pitch frequency. We can change which peak is being estimated by adjusting the Ni. However, this does not say which pitch dominates in marquee effect cases (see the following). — Eric J. Heller, Why You Hear What You Hear- An…, 2085 (#expands, via linked claim)

In each case, the highest common factor of the components is f0. This method also provides a basis for understanding how a pitch is perceived for non-harmonic sounds, such as bells or chime bars, whose components are not exact harmonics (integer multipliers) of the resulting f0. — David Howard, Jamie Angus, coustics and Psychoacoustics, 864 (#qualifies, via linked claim)

Categorization

• XREF: connects to: Pitch is a human synthesis keyed to autocorrelation, not pure physics; Pitch perception corresponds to peaks in the autocorrelation function — Eric J. Heller, Why You Hear What You Hear- An…, 2033; Autocorrelation predicts pitch yet met lukewarm reception — Eric J. Heller, Why You Hear What You Hear- An…, 2085 (+8 more)

Related

• Autocorrelation predicts pitch yet met lukewarm reception (similarity 0.95)
• Pitch perception corresponds to peaks in the autocorrelation function (similarity 0.92)
• Pitch is a human synthesis keyed to autocorrelation, not pure physics (similarity 0.92)
• The auditory system performs pitch analysis as autocorrelation per sub-band, summed into a correlogram. (similarity 0.91)
• Qualitative pitch mirrors the qualitative nature of autocorrelation peaks (similarity 0.90)
• Higher partials dominate pitch because their sharper autocorrelation peaks win (similarity 0.88)
• Autocorrelation nearly perfectly predicts perceived residue pitch across a range of tone series. (similarity 0.87)
• Perceived pitch is set primarily by fundamental frequency. (similarity 0.86)
• Autocorrelation models explain pitch and can separate concurrent vowel sounds differing in fundamental frequency (similarity 0.85)
• Autocorrelation, proposed in 1951, remains essential to pitch perception theory (similarity 0.85)
• Pitch perception depends on harmonics being integer multiples of the fundamental (similarity 0.85)
• Pitch perception can be modeled by autocorrelation across auditory filter bands (similarity 0.84)
• The central debate in pitch perception is whether humans are essentially walking Fourier analyzers (similarity 0.84)
• Highest common factor of partials encodes perceived pitch (similarity 0.83)
• Pitch resolution exceeds smeared bandwidth via peak-locating algorithms (similarity 0.83)

Broader Connections

• Autocorrelation predicts pitch yet met lukewarm reception #expands (0.90) — Eric J. Heller, Why You Hear What You Hear- An…, 2085
• Pitch perception depends on harmonics being integer multiples of the fundamental #qualifies (0.85) — David Howard, Jamie Angus, coustics and Psychoacoustics, 864
• Qualitative pitch mirrors the qualitative nature of autocorrelation peaks #expands (0.85)
• Autocorrelation models explain pitch and can separate concurrent vowel sounds differing in fundamental frequency #expands (0.80) — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 1658
• Pitch can be perceived even when no partial exists at that frequency #complicates (0.80) — Eric J. Heller, Why You Hear What You Hear- An…, 2031
• Clear pitch perception depends on periodic repetition of the pressure wave #complicates (0.80) — David Howard, Jamie Angus, coustics and Psychoacoustics, 774
• The central debate in pitch perception is whether humans are essentially walking Fourier analyzers #responds-to (0.75)