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A Structural Theory of Pitch(1,2,3).

Laudanski J, Zheng Y, Brette R - eNeuro (2014)

Bottom Line: For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics.While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments.We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut D'etudes De La Cognition, Ecole Normale Supérieure , Paris, France ; Scientific and Clinical Research Department, Neurelec , Vallauris, France.

ABSTRACT
Musical notes can be ordered from low to high along a perceptual dimension called "pitch". A characteristic property of these sounds is their periodic waveform, and periodicity generally correlates with pitch. Thus, pitch is often described as the perceptual correlate of the periodicity of the sound's waveform. However, the existence and salience of pitch also depends in a complex way on other factors, in particular harmonic content. For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics. Here we examine the theoretical proposition that pitch is the perceptual correlate of the regularity structure of the vibration pattern of the basilar membrane, across place and time-a generalization of the traditional view on pitch. While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments. We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.

No MeSH data available.


Pitch recognition by a neural network model based on the structural theory. A, Top, Spectrogram of a sequence of sounds, which are either either environmental noises (inharmonic) or musical notes of the chromatic scale (A3-A4) played by different instruments. Bottom, Firing rate of all pitch-specific neural groups responding to these sounds (vertical axis: preferred pitch, A3−A4). B, Distribution of firing rates of pitch-specific groups for instruments played at the preferred pitch (blue) and for noises (grey) for three different sound levels. C, Top, Pitch recognition scores of the model (horizontal axis: error in semitones) on a set of 762 notes between A2 and A4, including 41 instruments (587 notes) and five sung vowels (175 notes). Bottom, Firing rate of all pitch-specific groups as a function of the difference between presented f0 and preferred f0, for all sounds (solid black: average). Peaks appear at octaves (12 semitones) and perfect fifths (7 semitones). D, Impact of the number of frequency channels (top) and maximal delay δmax (bottom) on recognition performance.
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f5: Pitch recognition by a neural network model based on the structural theory. A, Top, Spectrogram of a sequence of sounds, which are either either environmental noises (inharmonic) or musical notes of the chromatic scale (A3-A4) played by different instruments. Bottom, Firing rate of all pitch-specific neural groups responding to these sounds (vertical axis: preferred pitch, A3−A4). B, Distribution of firing rates of pitch-specific groups for instruments played at the preferred pitch (blue) and for noises (grey) for three different sound levels. C, Top, Pitch recognition scores of the model (horizontal axis: error in semitones) on a set of 762 notes between A2 and A4, including 41 instruments (587 notes) and five sung vowels (175 notes). Bottom, Firing rate of all pitch-specific groups as a function of the difference between presented f0 and preferred f0, for all sounds (solid black: average). Peaks appear at octaves (12 semitones) and perfect fifths (7 semitones). D, Impact of the number of frequency channels (top) and maximal delay δmax (bottom) on recognition performance.

Mentions: We presented two types of natural sounds to this model (spectrograms shown in Fig. 5A, top): inharmonic sounds (e.g., an airplane, a sea wave, and street noise) and harmonic sounds (e.g., clarinet, accordion, and viola) with f0 between A2 and G4. For each sound, we measure the average firing rate of all pitch-tuned neuron groups (Fig. 5a, bottom). Inharmonic sounds generally produce little activation of these neurons, whereas harmonic sounds activate specific groups of neurons (with some octave confusions, see below). In Figure 5A, musical notes were played in chromatic sequence, which appears in the response of pitch-tuned groups. Figure 5B shows the distribution of group firing rates, measured in the entire neuron model, for inharmonic (grey) and harmonic sounds (blue), at three different sound levels. Although an increase in sound level produces an overall increase in population firing rate, there is little overlap between the rate distributions for harmonic and inharmonic sounds.


A Structural Theory of Pitch(1,2,3).

Laudanski J, Zheng Y, Brette R - eNeuro (2014)

Pitch recognition by a neural network model based on the structural theory. A, Top, Spectrogram of a sequence of sounds, which are either either environmental noises (inharmonic) or musical notes of the chromatic scale (A3-A4) played by different instruments. Bottom, Firing rate of all pitch-specific neural groups responding to these sounds (vertical axis: preferred pitch, A3−A4). B, Distribution of firing rates of pitch-specific groups for instruments played at the preferred pitch (blue) and for noises (grey) for three different sound levels. C, Top, Pitch recognition scores of the model (horizontal axis: error in semitones) on a set of 762 notes between A2 and A4, including 41 instruments (587 notes) and five sung vowels (175 notes). Bottom, Firing rate of all pitch-specific groups as a function of the difference between presented f0 and preferred f0, for all sounds (solid black: average). Peaks appear at octaves (12 semitones) and perfect fifths (7 semitones). D, Impact of the number of frequency channels (top) and maximal delay δmax (bottom) on recognition performance.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596137&req=5

f5: Pitch recognition by a neural network model based on the structural theory. A, Top, Spectrogram of a sequence of sounds, which are either either environmental noises (inharmonic) or musical notes of the chromatic scale (A3-A4) played by different instruments. Bottom, Firing rate of all pitch-specific neural groups responding to these sounds (vertical axis: preferred pitch, A3−A4). B, Distribution of firing rates of pitch-specific groups for instruments played at the preferred pitch (blue) and for noises (grey) for three different sound levels. C, Top, Pitch recognition scores of the model (horizontal axis: error in semitones) on a set of 762 notes between A2 and A4, including 41 instruments (587 notes) and five sung vowels (175 notes). Bottom, Firing rate of all pitch-specific groups as a function of the difference between presented f0 and preferred f0, for all sounds (solid black: average). Peaks appear at octaves (12 semitones) and perfect fifths (7 semitones). D, Impact of the number of frequency channels (top) and maximal delay δmax (bottom) on recognition performance.
Mentions: We presented two types of natural sounds to this model (spectrograms shown in Fig. 5A, top): inharmonic sounds (e.g., an airplane, a sea wave, and street noise) and harmonic sounds (e.g., clarinet, accordion, and viola) with f0 between A2 and G4. For each sound, we measure the average firing rate of all pitch-tuned neuron groups (Fig. 5a, bottom). Inharmonic sounds generally produce little activation of these neurons, whereas harmonic sounds activate specific groups of neurons (with some octave confusions, see below). In Figure 5A, musical notes were played in chromatic sequence, which appears in the response of pitch-tuned groups. Figure 5B shows the distribution of group firing rates, measured in the entire neuron model, for inharmonic (grey) and harmonic sounds (blue), at three different sound levels. Although an increase in sound level produces an overall increase in population firing rate, there is little overlap between the rate distributions for harmonic and inharmonic sounds.

Bottom Line: For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics.While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments.We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institut D'etudes De La Cognition, Ecole Normale Supérieure , Paris, France ; Scientific and Clinical Research Department, Neurelec , Vallauris, France.

ABSTRACT
Musical notes can be ordered from low to high along a perceptual dimension called "pitch". A characteristic property of these sounds is their periodic waveform, and periodicity generally correlates with pitch. Thus, pitch is often described as the perceptual correlate of the periodicity of the sound's waveform. However, the existence and salience of pitch also depends in a complex way on other factors, in particular harmonic content. For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics. Here we examine the theoretical proposition that pitch is the perceptual correlate of the regularity structure of the vibration pattern of the basilar membrane, across place and time-a generalization of the traditional view on pitch. While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments. We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.

No MeSH data available.