The auditory system can be modeled as computing autocorrelation functions per frequency band

Evidence

Figure 13.11 shows the general concept for autocorrelation-based pitch analysis. The signals originating from the filter bank are first processed by a hair-cell model consisting of a half-wave rectifier and a subsequent low-pass filter. A separate autocorrelation function (ACF) is then computed for each signal in order to detect the periodicities in the different sub-bands. The ACF presentation is often called a correlogram. Thereafter, the separate ACFs are summed to obtain the summary autocorrelation function, characterizing the periodicities in the original stimulus. — Ville Pulkki, Matti Karjalainen, Communication Acoustics- An…, 1654

Categorization

Category: Definitional Content

• DEFINE: Defines the correlogram and summary autocorrelation function, the computational backbone of temporal pitch models.

Related

• Pitch perception can be modeled by autocorrelation across auditory filter bands (similarity 0.96)
• The auditory system performs pitch analysis as autocorrelation per sub-band, summed into a correlogram. (similarity 0.89)
• Autocorrelation reveals a signal’s fundamental periodicity through periodic peaks. (similarity 0.84)
• Across-filter neural synchrony provides a robust pitch code (similarity 0.83)
• Pitch perception is encoded by the timing of neural firing in the cochlea (similarity 0.83)
• Auditory nerve phase locking can encode slow waveform fluctuations across filters (similarity 0.83)
• Pitch perception corresponds to peaks in the autocorrelation function (similarity 0.83)
• Autocorrelation predicts pitch yet met lukewarm reception (similarity 0.83)
• Autocorrelation predicts perceived pitch of non-harmonic partial groups (similarity 0.83)
• Dominant pitch models derive pitch from auditory nerve spike timing. (similarity 0.83)
• Autocorrelation, proposed in 1951, remains essential to pitch perception theory (similarity 0.82)
• Qualitative pitch mirrors the qualitative nature of autocorrelation peaks (similarity 0.82)
• Autocorrelation nearly perfectly predicts perceived residue pitch across a range of tone series. (similarity 0.81)
• Autocorrelation models explain pitch and can separate concurrent vowel sounds differing in fundamental frequency (similarity 0.81)
• Hearing-modeling spectrograms use an ERB frequency axis from modeled auditory filters. (similarity 0.81)