theories & models of musical pitchdspace.mit.edu/bitstream/handle/1721.1/59549/hst...the search...
TRANSCRIPT
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Theories & models of musical pitch
Harvard-MIT Division of Health Sciences and TechnologyHST.725: Music Perception and CognitionProf. Peter Cariani
www.cariani.com
(Image removed due to copyright considerations.)
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The search for the missing fundamental: theories & models of musical pitch
• Brief review of basics of sound & vibration• Brief review of pitch phenomena• Distortion theories (nonlinear processes produce F0 in the cochlea)• Spectral pattern theories
– Pattern-recognition/pattern-completion– Fletcher: frequency separation – The need for harmonic templates (Goldstein)
Terhardt’s Virtual pitch: adding up the subharmonics– Musical pitch equivalence classes– Pitch classes and neural nets: Cohen & Grossberg– Learning pitch classes with connectionist nets: Bharucha
• Temporal theories– Residues: Beatings of unresolved harmonics (Schouten, 1940’s)– Problems with residues and envelopes– Temporal autocorrelation models (Licklider, 1951)– Interspike interval models (Moore, 1980)– Correlogram demonstration (Slaney & Lyon, Apple demo video)– Population-interval models (Meddis & Hewitt, Cariani & Delgutte)
• Problems & prospects
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Vibrations create compressions and expansions of air
Sound waves are alternating local changes in pressure
These changes propagate through space as “longitudinal” waves
Condensation phase (compression):pressure increases
Rarefaction (expansion) phase:pressure decreases
(Series of figures from Handel, S. 1989. Listening: an Introduction to the Perception of Auditory Events. MIT Press. Used with permission.)
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WaveformsMicrophones convert sound pressures to electrical voltages.Waveforms plot pressure as a function of time, i.e. a “time-series” of amplitudes.Waveforms are complete descriptions of sounds.
Audio CD’s sample sounds at 44,100 samples/sec.
Oscilloscope demonstration.
5
p
t10 ms
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Complex modes of vibration
Most physical systems have multiple modes of vibrations that create resonances that favor particular sets of frequencies.
Vibrating strings or vibrating columns of air in enclosures exhibit harmonic resonance patterns.
Material structures that are struck (bells, xylophones, percussive instruments) have resonances that depend partly on their shape and therefore canproducefrequencies that are not harmonically related.
More later on what this means forpitch and sound quality.
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Frequency spectraJoseph Fourier (1768-1830) showed that any waveform can be
represented as the sum of many sinusoids (Fourier spectrum). George Ohm (1789-1854) and Hermann von Helmholtz(1821-1894) postulated that the ear analyzes sound
analogously, first breaking sounds into their partials.
Each sinusoid of a particular frequency (frequency component, partial) has 2 parameters:– 1) its magnitude (amplitude of the sinusoid)– 2) its phase (relative starting time)
A sound with only one frequency component is called a pure tone.A sound with more than one is called a complex tone.
A complex tone whose partials are all part of the same harmonic series is called a harmonic complex.
Such a tone is periodic -- its waveform repeats with a period equal to 1/fundamental frequency (i.e. the fundamental period).
If any of the partials are not part of a harmonic series, then the sound isinharmonic.
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Fundamentals and harmonics
• Periodic sounds (30-20kHz) produce pitch sensations.
• Periodic sounds consist of repeating time patterns.
• The fundamental period (F0) is the duration of the repeated pattern.
• The fundamental frequency is the repetition frequency of the pattern.
• In the Fourier domain, the frequencycomponents of a periodic sound are all members of a harmonic series (n = 1*F0, 2*F0, 3*F0...).
• The fundamental frequency is therefore the greatest common divisor of all of the component frequencies.
• The fundamental is also therefore a subharmonic of all component frequencies.
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Demonstrations
• Oscilloscope demonstrations• Armenian flute• Human voice
• Spectrum analyzer demonstrations• Flute• Violins• Human voice
• Real time spectrogram demonstrations (iTunes/SpectroGraph plugin)• Armenian flute• Violins• Vocal music
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Harmonic series
A harmonic series conists of integer multiples of a fundamental frequency, e.g. if the fundamental is 100 Hz, then the harmonic series is: 100, 200, 300, 400, 500, 600 Hz, .... etc.
The 100 Hz fundamental is the first harmonic, 200 Hz is the secondharmonic. The fundamental is often denoted by F0.
The fundamental frequency is therefore the greatest common divisor of all the frequencies of the partials.
Harmonics above the fundamental constitute the overtone series.
Subharmonics are integer divisions of the fundamental:e.g. for F0= 100 Hz, subharmonics are at 50, 33, 25, 20, 16.6 Hz etc.Subharmonics are also called undertones.
The fundamental period is 1/F0, e.g. for F0=100 Hz, it is 1/100 sec or 10 msec. Periods of the subharmonics are integer multiples of the fundamental period.
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Synthetic vowelsWaveforms Power Spectra Autocorrelations
Pitch periods, 1/F0
0 1 2 3 4Frequency (kHz)
0 10 20Time (ms)
0 5 10 15Interval (ms)
100 Hz125 HzFormant-relatedVowel qualityTimbre
[ae]F0 = 100 Hz
[ae]F0 = 125 Hz
[er]F0 = 100 Hz
[er]F0 = 125 Hz
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Pitch : basic properties• Highly precise percepts
– Musical half step: 6% change F0– Minimum JND's: 0.2% at 1 kHz (20 usec time difference, comparable to ITD jnd)
• Highly robust percepts– Robust quality Salience is maintained at high stimulus intensities– Level invariant (pitch shifts < few % over 40 dB range)– Phase invariant (largely independent of phase spectrum, f < 2 kHz)
• Strong perceptual equivalence classes– Octave similarities are universally shared– Musical tonality (octaves, intervals, melodies) 30 Hz - 4 kHz
• Perceptual organization (“scene analysis”)– Fusion: Common F0 is a powerful factor for grouping of frequency components
• Two mechanisms? Temporal (interval-based) & place (rate-based)– Temporal: predominates for periodicities < 4 kH (level-independent, tonal)– Place: predominates for frequencies > 4 kHz(level-dependent, atonal)
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Periodic sounds produce distinct pitches
Many different sounds produce the same pitches
Strong• Pure tones• Harmonic complexes• Iterated noise
Weaker• High harmonics• Narrowband noise
Very weak• AM noise• Repeated noise
Strongpitches
Weakerlow
pitches
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Pitch as a perceptual emergent
0 200 400 600 800 1000 1200 1400 1600 1800 2000
0 5 10 15 200
5
10
0 5 10 15 20
600
Pure tone200 Hz
0 200 400 600 800 10001200140016001800200
MissingF0
Line spectra Autocorrelation (positive part)
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Frequency ranges of (tonal) musical instruments
27 Hz 4 kHz262Hz
440Hz
880Hz
110Hz
10k86543210.50.25
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Duplex time-place representations"Pitch is not simply frequency"
Musical tonality: octaves, intervals, melodies
Strong phase-locking (temporal information)
temporal representationlevel-invariant, precise
place representationlevel-dependent, coarse
30 100 1k 10k
Frequency (kHz)
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Pitch height and pitch chroma
Please see Figure 1, 2, and 7 in Roger N. Shepard. "Geometrical Approximations to the Structure of Musical Pitch." Psychological Review 89 no. 4 (1982): 305-322.
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Duplex time-place representations
temporal representationlevel-invariant
• strong (low fc, low n)• weak (high fc, high n; F0 < 100 Hz)
place-basedrepresentation
level-dependentcoarse
30 100 1k 10k
Similarity to place pattern
Similarity to
intervalpattern
cf. Terhardt'sspectral and virtual pitch
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A "two-mechanism" perspective (popular with some psychophysicists)
unresolved harmonicsweak temporal mechanism
phase-dependent; first-order intervals
30 100 1k 10k
place-basedrepresentationslevel-dependent
coarseharm
onic
num
ber
place-basedrepresentation
level-independentfine
resolved harmonicsstrong spectral pattern mechanism
phase-independentrate-place? interval-place?
Dominance regionf, F0
n= 5-10
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Rate-place
1/F0
Population interval
Interval
Population-interval
Central spectrum
CF
Central spectrum
CF
Synchrony-place
Interval-place
Local
Global
Somepossibleauditoryrepresentations
Peristimulus time (ms)
10
1
Cha
ract
eris
tic fr
eq. (
kHz)
0 5010 20 30 40
Masking phenomenaLoudness
Pure tone pitch JNDs: Goldstein
Complex tone pitch
Phase-place
Stages of integration
All-at-once
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General theories of pitch
1. Distortion theories– reintroduce F0 as a cochlear distortion component (Helmholtz)–sound delivery equipment can reintroduce F0 through distortion–however, masking F0 region does not mask the low pitch (Licklider)–low pitch thresholds and growth of salience with level not consistent with distortion processes (Plomp, Small)–binaurally-created pitches exist
2. Spectral pattern theories–Operate in frequency domain–Recognize harmonic relationson resolved components
3. Temporal pattern theories–Operate in time domain–Analyze interspike interval dists.
AutocorrelationrepresentationTime domain
Stimulus
array of cochlear band-pass filters
interspike intervalsdischarge rates
F0 = 200 HzF0 = 160 HzF0 = 100 Hz
harmonic templates
frequency(linear scale)optimal
match
auditorynerve fibertuning curves
0 155 10Interval (ms)
# in
terv
als
1/F01/F1
Populationinterspike interval
distribution
Pitch → best fitting template
Population rate-place profile
0 3
dB
Frequency (kHz)
50
0
F0= 80 Hz
Power spectrumrepresentation
Frequency domain
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Psychological perspectives on pitch
Analytical: break sounds into frequencies(peceptual atoms), then analyze patterns (templates)(British empiricism; machine perception)
Relational: extract invariant relations from patterns(Gestaltists, Gibsonians, temporal models)
Nativist/rationalist: mechanisms for pitch are given by innateknowledge and/or computational mechanismsdifferences re: how recently evolved these are
Associationist: mechanisms for pitch (e.g. templates) must beacquired through experience (ontogeny, culture)
Interactionist: (Piaget) interaction between native faculties and structure of experience (self-organizing systems)
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Spectral pattern theories Central spectrum• Not the lowest harmonic• Not simple harmonic spacings• Not waveform envelope or peak-picking
(pitch shift exps by Schouten & de Boer)
• Must do a real harmonic analysis of spectral fine structure to find common denominator, which is the fundamental frequency
• Terhardt: find common subharmonics• Wightman: autocorrelation of spectra• Goldstein, Houtsma: match spectral
excitation pattern to harmonic templates• SPINET: Use lateral inhibition/center-
surround then fixed neural net to generate equivalence classes
• Barucha: adaptive connectionist networks for forming harmonic associations
F0 = 200 HzF0 = 160 HzF0 = 100 Hz
harmonic templates
frequency(linear scale)optimal
match
Pitch → best fitting template
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Spectral pattern analysis
vs.temporal pattern
analysis
Note: Some models,such as Goldstein'suse interspike intervalinformation to first forma Central Spectrumwhich is then analyzed usingharmonic spectral templates.
There are thus dichotomies1) between use oftime and placeinformationas the basis of the centralrepresentation, and2) use of spectral vs. autocorrelation-like central representations
AutocorrelationrepresentationTime domain
Stimulus
array of cochlear band-pass filters
interspike intervalsdischarge rates
F0 = 200 HzF0 = 160 HzF0 = 100 Hz
harmonic templates
frequency(linear scale)optimal
match
auditorynerve fibertuning curves
0 155 10Interval (ms)
# in
terv
als
1/F01/F1
Populationinterspike interval
distribution
Pitch → best fitting template
Population rate-place profile
0 3
dB
Frequency (kHz)
50
0
F0= 80 Hz
Power spectrumrepresentation
Frequency domain
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Ear and cochlea
Tympanic Membrane
Stapes on Oval Window
Cochlear Base
Scala Vestibuli
Basilar Membrane
Relative A
mplitude
Scala Tympani “Unrolled” Cochlea
Cochlear Apex
Helicotrema
Narrow Base of Basilar Membraneis “tuned” for highfrequencies
Wider apex is“tuned” for lowfrequencies
Distance from Stapes (mm)
1600 Hz
800 Hz
400 Hz
200 Hz
100 Hz
50 Hz
25 Hz
0 10 20 30
Traveling waves along the cochlea. A traveling wave is shown at a given instant along the cochlea, which has been uncoiled for clarity. The graphs profile the amplitude of the traveling wave along the basilar membrane for different frequencies, and show that the position where the traveling wave reaches its maximum amplitude varies directly with the frequency of stimulation.(Figures adapted from Dallos, 1992 and von Bekesy, 1960)
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“Virtual” pitch: F0-pitch as pattern completion
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From masking patterns to "auditory filters" as amodel of hearing
Power spectrumFilter metaphor
Notion of one centralspectrum that subservesperception of pitch, timbre,and loudness
2.2. Excitation pattern Using the filter shapes and bandwidths derived from masking experiments we can produce the excitation pattern produced by a sound. The excitation pattern shows how much energy comes through each filter in a bank of auditory filters. It is analogous to the pattern of vibration on the basilar membrane. For a 1000 Hz pure tone the excitation pattern for a normal and for a SNHL listener look like this: The excitation pattern to a complex tone is simply the sum of the patterns to the sine waves that make up the complex tone (since the model is a linear one). We can hear out a tone at a particular frequency in a mixture if there is a clear peak in the excitation pattern at that frequency. Since people suffering from SNHL have broader auditory filters their excitation patterns do not have such clear peaks. Sounds mask each other more, and so they have difficulty hearing sounds (such as speech) in noise. --Chris Darwin, U. Sussex, http://www.biols.susx.ac.uk/home/Chris_Darwin/Perception/Lecture_Notes/Hearing3/hearing3.html
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Shapes of perceptually-derived "auditory filters" (Moore)
0 c
b
d
a
ee
d
c
b
a
-10
-20
-30
-40
-50
0
-10
-20
-30
-40
-500.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0
90
80
70
60
50
40
30
20
10
00.5 1 2 5 10
Rel
ativ
e ga
in, d
B
Rel
ativ
e E
xcita
tion
Leve
l, dB
Frequency, kHz Filter Center Frequency, kHzFrequency, kHz (log scale)
Exc
itatio
n Le
vel,
dB
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Resolution of harmonics
00 1000 2000 3000 4000 Hz
10
20
30
40Th
resh
old
Shi
ft (d
B)
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Goldstein’sharmonic templates
Please see Figure 3 in Goldstein J. L., A. Gerson, P. Srulovicz, M. Furst. "Verification of the Optimal Probabilistic Basis of Aural Processing in Pitch of Complex Tones." J AcoustSoc Am. 63, no. 2 (Feb, 1978): 486-97.
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Julius Goldsteinreferences
Models forpure tonepitchdiscrimination,low pitches ofcomplex tones,binaural pitches,andaural distortionproducts
Goldstein JL (1970) Aural combination tones. In: Frequency Analysis and PeriodicityDetection in Hearing (Plomp R, Smoorenburg GF, eds), pp 230-247. Leiden: A.W. Sijthoff.
Goldstein JL (1973) An optimum processor theory for the central formation of the pitchof complex tones. J Acoust Soc Am 54:1496-1516.
Goldstein JL, Kiang NYS (1968) Neural correlates of the aural combination tone 2f1-f2.IEEE Proc 56:981-992.
Goldstein JL, Srulovicz P (1977) Auditory-nerve spike intervals as an adequate basis foraural frequency measurement. In: Psychophys ics and Physiology of Hearing(Evans EF, Wilson JP, eds). London: Academic Press.
Goldstein JL, Buchsbaum G, First M (1978a) Compatibility between psychophys ical andphysiological measurements of aural combination tones. J Åcou st Soc Am63:474-485.
Goldstein JL, Buchsbaum G, Furst M (1978b) Compatibility between psychophys ical andphysiological measurements of aural combination tones... Journal of theAcoustical Society of America 63:474-485.
Goldstein JL, Gerson A, Srulovicz P, Furst M (1978c) Verification of the optimalprobabilistic basis of aural processing in pitch of complex tones. J Acoust Soc Am63:486-510.
H. L. Duifhuis and L. F. Willems and R. J. Sluyter ( 1982,) Measurement of pitch inspeech: An implementation of Goldstein's theory of pitch perception,. jasa, 71,:1568--1580.
Houtsma AJM, Goldstein JL (1971) Perception of musical intervals: Evidence for thecentral origin of the pitch of complex tones. In: M.I.T./R.L.E.
Houtsma AJM, Goldstein JL (1972) The central origin of the pitch of complex tones:Evidence from musical interval recognition. J Acoust Soc Am 51:520-529.
P. Srulovicz and J. Goldstein ( 1983) A central spectrum model: A synthesis ofauditory nerve timing and place cues in monoaural communication offrequencyspectrum,. jasa, 73,: 1266--1276,.
Srulovicz P, Goldstein JL (1977) Central spectral patterns in aural signal analysis basedon cochlear neural timing and frequency filtering. In: IEEE, p 4 pages. Tel Aviv,Israel.
Srulovicz P, Goldstein JL (1983) A central spectrum model: a synthesis of auditory-nervetiming and place cues in monaural communication of frequency spectrum. JAcoust Soc Am 73:1266-1276.
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Terhard's method of common subharmonics
Spectral vs. virtual pitch: duplex modelVirtual pitch computation:1. Identify frequency components2. Find common subharmonics3. Strongest common subharmonic after
F0 weighting is the virtual pitchTerhardt's model has been extended by
Parncutt to cover pitch multiplicityand fundamental bass of chords
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Terhardt references
Terhardt E (1970) Frequency analysis and periodicity detection in the sensations of roughness and periodicity pitch. In: Frequency Analysis and Periodicity Detection in Hearing (Plomp R, Smoorenburg GF, eds). Leiden: A. W. Sijthoff.
Terhardt E (1974a) On the perception of periodic sound fluctuations (roughness). Acustica 30:201-213.
Terhardt E (1974b) Pitch, consonance, and harmony. J Acoust Soc Am 55:1061-1069. Terhardt E (1977) The two-component theory of musical consonance. In: Psychophysics
and Physiology of Hearing (Evans EF, Wilson JP, eds), pp 381-390. London: Academic Press.
Terhardt E (1979) Calculating virtual pitch. Hearing Research 1:155-182. Terhardt E (1984) The concept of musical consonance: a link between music and
psychoacoustics. Music Perception 1:276-295. Terhardt E, Stoll G, Seewann M (1982a) Pitch of complex signals according to virtual-
pitch theory: test, examples, and predictions. J Acoust Soc Am 71:671-678. Terhardt E, Stoll G, Seewann M (1982b) Algorithm for extraction of pitch and pitch
salience from complex tonal signals. J Acoust Soc Am 71:679-688. Parncutt R (1989) Harmony: A Psychoacoustical Approach. Berlin: Springer-Verlag.
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SPINET: Cohen Grossberg, Wyse JASA
Fixed neuralnetwork:connection weights arrangedso as to formpitch-equivalenceclasses
Please see Cohen M. A., S. Grossberg, L. L. Wyse. "A Spectral Network Model of Pitch Perception." J AcoustSoc Am. 98no. 2 Pt 1 (Aug, 1995): 862-79.
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Neural tuning as a function
of CF
10
8
6
4
2
00
20
40
60
80
100
120
0.1
Characteristic frequency (kHz)
Q 1
0dB
Thre
shol
d to
ne le
vel (
dB S
PL)
1.0 10
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Broad tuning and rate saturation at moderate levels in low-CF auditory nerve fibers confounds rate-based resolution of harmonics.
Low SR auditory nerve fiber
0300 500 1000
Frequency (Hz)
Spik
es/s
1500 2000
85 dB75 dB
65 dB
55 dB45 dB
35 dB
25 dB
95 dB
B.F. = 1700 Hz
Spon.act.
No sync.
2500 3000
50
100
150
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Spectral pattern theories - pros & cons
Do make use of frequency tuning properties of elements in the auditory system
No clear neural evidence of narrow (< 1/3 octave) frequency channels in low-BF regions (< 2 kHz)
Operate on perceptually-resolved harmonicsDo not explain low pitches of unresolved harmonics
Require templates or harmonic pattern analyzersLittle or no neural evidence for required analyzersProblems w. templates: relative nature of pitch
Do not explain well existence region for F0Learning theories don't account for F0 ranges
or for phylogenetic ubiquity of periodicity pitch
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Existence region for missing fundamentals of AM tones
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ANFs
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Temporal pattern theoriesΣ First-order intervals(renewal density)
• Schouten’s temporal theory (1940’s) depended on interactions between unresolved (high) harmonics. It was displaced by discovery of dominance region and binaural combination pitches in the 1960’s. The idea persists, however in the form of spectral mechanisms for resolved harmonics and temporal ones for unresolved harmonics.
Please see van Noorden, L. "Two channel pitch perception." In Music, Mind and Brain. Editedby M. Clynes. New York: Plenum.
Licklider (1951)Σ All-order intervals (temporal autocorrelation)
Please see Moore, BCJ. An Introduction to the Psychology of Hearing. 5th ed. San Diego: Academic Press. 2003.
Please see Figure 1 in Meddis, R., and M. J. Hewitt. Virtual pitch and phase sensitivity of a computer model of the Auditory periphery. I. Pitch identification. J. Acoust. Soc. Am. 89 no. 6 (1991): 2866-2882.
B1
C1
A
C2 C3 C4 C5 C6
D1 D2
T
D3 D4 D5 D6
B2 B3 B4 B5 B6 B7
Basic schema of neuronal autocorrelator. A is the inputneuron, B1, B2, B3, ..... is a delay chain.
.....
.....
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Interval-basedtheories of pitchFirst-order intervals
(renewal density)
Please see van Noorden, L. Two channel pitch perception. In Music, Mind and Brain. Editedby M. Clynes. New York: Plenum.
Please see Moore, BCJ. An introduction to the psychology of hearing. 5th ed.San Diego: Academic Press. 2003.
All-order intervals(temporal autocorrelation)
Licklider (1951)
Please see Figure 1 in Meddis, R., and M. J. Hewitt. Virtual pitch and phase sensitivity of a computer model of the Auditory periphery. I. Pitchidentification. J. Acoust. Soc. Am. 89 no. 6(1991): 2866-2882.
B1
C1
A
C2 C3 C4 C5 C6
D1 D2
T
D3 D4 D5 D6
B2 B3 B4 B5 B6 B7
Basic schema of neuronal autocorrelator. A is the inputneuron, B1, B2, B3, ..... is a delay chain.
.....
.....
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Licklider’s (1951) duplex model of pitch perception
Licklider’s binaural triplex model
J.C.R. Licklider“Three AuditoryTheories” In Psychology: A Study of a Science. Vol. 1, S. Koch, ed., McGraw-Hill, 1959, pp. 41-144.
B1
C1
A
C2 C3 C4 C5 C6
D1 D2
T
D3 D4 D5 D6
B2 B3 B4 B5 B6 B7
Basic schema of neuronal autocorrelator. A is the inputneuron, B1, B2, B3, ..... is a delay chain.
.....
.....
(Image removed due to copyright considerations.)
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Delay lines
B1
C1
A
C2 C3 C4 C5 C6
D1 D2
T
D3 D4 D5 D6
B2 B3 B4 B5 B6 B7
Basic schema of neuronal autocorrelator. A is the inputneuron, B1, B2, B3, ..... is a delay chain.
.....
.....
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Autocorrelation and interspike intervals
1/F0
Fundamentalperiod
time lagτ
S(t ) S( t - τ)Corr(τ) =Σt
ShiftMultiplySum the productsfor each delay τto computeautocorrelationfunction
Autocorrelation functions = Histograms ofall-order intervals
Autocorrelationsof spike trains
0000001000000000100000000000000000000100000000001000000000000010000000001000000000000000000001000000000010000000
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Please see Figure 6.16A-D in Lyon, Richard, and ShihabShamma. "Auditory Representations of Timbre and Pitch." InAuditory Computation. Edited by R. R. Fay. New York:Springer Verlag. 1996.
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Correlograms: interval-place displays (Slaney & Lyon)
Please see Slaney, Malcolm, and Richard F. Lyon. "On theImportance of Time -A Temporal Representation of Sound."In Visual Representations of Speech Signals. Editedby M. Crawford. New York: John Wiley. 1993.
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Correlograms
Please see Slaney, Malcolm, and Richard F. Lyon. "On theImportance of Time -A Temporal Representation of Sound."In Visual Representations of Speech Signals. Editedby M. Crawford. New York: John Wiley. 1993.
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Auditory nerve
Peristimulus time (ms)
10
1
Cha
ract
eris
tic fr
eq. (
kHz)
0 5010 20 30 40
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Temporal codingin the auditory nerve
Work with Bertrand DelgutteCariani & Delgutte (1996ab)
Dial-anesthetized cats.100 presentations/fiber60 dB SPL
Population-interval distributions are compiled by summing together intervals from all auditory nerve fibers.
The most common intervals present in the auditory nerve are invariably related to the pitches heard at the fundamentals of harmonic complexes.
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The population-interval distribution of the auditory nerveStimulus AutocorrelationStimulus waveform 1/F0
Pitch =1/F0
Interval (ms)151050
# in
terv
als
500
Peristimulus time (ms)
# sp
ikes
250
290280260240 270250
1/F0
.5
.2
1
2
5
Cha
ract
eris
tic fr
eque
ncy
(kH
z)Post-stimulus time histograms All-order interval histograms
Population-interval histogramPopulation-PST histogram
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Stimulus Autocorrelation Pitch =1/F0
Interval (ms)151050
# in
terv
als
500
All-order interval histograms
Population-interval histogram
Cha
ract
eris
tic fr
eque
ncy
(kH
z)
.5
.2
1
2
5
1/F0
Fundamentalperiod
time lagτ
S( t ) S( t - τ)Corr(τ) =Σt
ShiftMultiplySum the productsfor each delay τto computeautocorrelationfunction
Autocorrelation functions
= Histograms ofall-order intervals
Autocorrelationsof spike trains
0000001000000000100000000000000000000100000000001000000000000010000000001000000000000000000001000000000010000000
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Percept-driven search for neural codes:1. Use stimuli that produce equivalent percepts2. Look for commonalities in neural response3. Eliminate those aspects that are not invariant
Neuralresponsepattern
Metamericstimuli
(same percept,different power spectra)
Stimulus A(AM tone, fm = 200 Hz)
Stimulus B(Click train, F0 = 200 Hz)
Common aspectsof neural response
that covarywith the percept
of interest
Candidate"neural codes"
or representations
Other parametersfor which percept is
invariant
Neural codes, representations:those aspects of the
neural responsethat play a functional role
in subservingthe perceptual distinction
Intensity
Location
Duration
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Populationinterval
distribution
Interspikeinterval (ms)
150
Six stimuli that produce a low pitch at 160 Hz
Pure tone 160 Hz
AM tone Fm:160 Hz Fc:640 Hz
Harms 6-12
AM tone
Click train
AM noise
F0 :160 Hz
Fm :160 HzFc:6400 Hz
Fm :160 Hz
Waveform Power spectrum Autocorrelation
Pitch frequency Pitch period
F0 : 160 Hz
Lag (ms)Frequency (Hz)TIme (ms)150
WEAKPITCHES
STRONGPITCHES
Pitch equivalence
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Level-Invariance Pitch Equivalence
0
1
2
2
2
1
2
5
Interval (ms)
400
1000
150
0 25All-Order Interval (msec) Interval (ms)
10 15 0 5 10 15
# In
terv
als
40 db SPL
60 db SPL
80 db SPL
E Pure Tone F=160 Hz
Neural Data Autocorrelation
Fc=640 Hz, Fm=160Hz
Fm=160Hz
F0=160 Hz
F AM Tone
G Click Train
H AM Noise
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The running population-interval distribution
Population intervalhistograms
(cross sections)
1000
0 155 10
Interval (ms)
# in
terv
als A
Interval (ms)
1000
0 155 10# in
terv
als B
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Pitch height and pitch chroma
Please see Figure 1, 2, and 7 in Roger N. Shepard."Geometrical Approximations to the Structure of Musical Pitch."Psychological Review 89 no. 4 (1982): 305-322.
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Pitch shift of inharmonic complex tones
800
800
800
800 1 /F m
300 340320
260 300280
220 260240
* *
* *
Interval (ms)0 5 10 15
Lag (ms)0 5 10 15
Peristimulus time (ms)340 380360
1/Fm
Population intervaldistributionsStimulus autocorrelationStimulus waveform
1/Fm
0 1
Freq (kHz)0 1
0 1
0 1
Fm = 125 HzFc = 750 Hz
n = 5.86
n = 5.5
n = 5
n=6
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Pitch shift of inharmonic complex tonesPhase-invariant nature of all-order interval code
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Cochlear nucleus IV: Pitch shift
.
Unit 35-40 CF: 2.1 kHzThr: 5.3 SR: 17.7
5
10
15Chop-S (PVCN)
45-15-8 CF:4417Thr: -18, SR: 39 s/s
10
20
30
0 500100 200 300 400Peristimulus time (ms)
Pauser (DCN)45-17-4 CF: 408 HzThr: 21.3 SR: 159
Inte
rval
(ms)
0 500100 200 300 400
30
Primarylike(AVCN)
Peristimulus time (ms)
de Boer'srule
(pitches)15sub-
harmonicsof Fc
1/Fc
de Boer'srule
(pitches)
5
10
15
Inte
rval
(ms)
1/Fm
Fm = 125 Hz Fc = 500-750 Hz Pitch ~ de Boer's ruleV ariable-Fc AM t one
500 500750 Fc(Hz)
Pooled ANF (n=47)
0 100 200 300 4000 500100 200 300 400
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Dominance region for pitch (harmonics 3-5 or partials 500-1500 Hz)
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n = 6 5.86 5.5
AM tone Power spectrum Autocorrelation
PitchEquivalence
Pitch of the"missingfundamental"
Level invariance
Phase invariance
Dominance region
Pitch shift ofinharmonicAM tones
Pitch salienceV. weak pitchWeak pitchStrong pitch
40 dB SPL 60 80
Population-interval representation of pitchat the level of the auditory nerve
Pure tone AM tone Click train AM noise
AM tone
QFM tone
1/F06-12 1/F03-5
F03-5=160 Hz 240 Hz 320 Hz 480 Hz
Summary
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Temporal theories - pros & cons
Make use of spike-timing properties of elements in early processing (to midbrain at least)
Interval-information is precise & robust & level-insensitive
No strong neurally-grounded theory of how this information is used
Unified model: account for pitches of perceptually-resolved & unresolved harmonics in an elegant way (dominant periodicity)
Explain well existence region for F0 (albeit with limits on max interval durations)
Do not explain low pitches of unresolved harmonics
Interval analyzers require precise delays & short coincidence windows
Little or no neural evidence for required analyzers
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Physiological and functional representations
Rate-place profiles
Interval-place Spatiotemporal pattern
(Place & time)Global interval
Central spectrum Frequency-domain
representation
Central autocorrelation Time-domain
representation
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Different representations can support analogous strategies for pitch extraction, recognition, and comparison
Central spectrum Frequency-domain
representation
Central autocorrelation Time-domain
representation
Explicitidentificationof individualharmonics &
deduction of F0 via common
subharmonics or pattern recognition
Template-based global recognition of
pitch-related patterns (neural networks,
harmonic templates, interval sieves)
Relative pitchcomparison mechanisms (matching,
octaves, musical intervals)
Spectral patterns Temporal patterns
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Reading/assignment for next meeting
• Tuesday. Feb. 24• Pitch mechanisms, continued• Perception of timbre
• Reading:Moore Chapter 8, pp. 269-273Chapter in Deutsch by Risset & Wessel on Timbre
(first sections up to p. 113-118)Handel, chapter on Identification
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Pitch classesand perceptual similarity
Harmonic similarity relationsare direct consequencesof the inherent structure
of interval codes
Build up harmonic associations
from repeatedexposure to harmonic
complex tones
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OCTAVESIMILARITY
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Octavesimilarity
Please see Cariani, Peter. Journal of New Music Research30 no. 2 (2001): 107-135.
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Pitch height and pitch chroma
Please see Figure 1, 2, and 7,in Roger N. Shepard."Geometrical Approximations to the Structure of MusicalPitch." Psychological Review 89 no. 4 (1982): 305-322.
Please see Cariani, Peter. Journal of New MusicResearch. 30 no. 2 (2001): 107-135.
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Musical tonal relations
Please see Roger N. Shepard. "Geometrical Approximationsto the Structure of Musical Pitch." Psychological Review89 no. 4 (1982): 305-322.
Please see Cariani, Peter. Journal of New MusicResearch. 30 no. 2 (2001): 107-135.