Residue pitches from two-tone complexes

Sutton, Ronald; Williams, Ronald J.

doi:10.1016/s0022-460x(70)81173-7

Cited by 2 publications

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“…The strongest evidence in favour of the spectral-pattern approach is that the prominence of residue pitch is greater the better the auditory resolution of component frequencies: Such a relationship is contrary to the predictions of a periodicity theory. This relationship is apparent in Ritsma's (1962) investigation of the existence region of residue pitch, in the dominance of low harmonic components (Plomp, 1976;Ritsma, 1967), in the limits to the audibility of residue pitch for two-component signals Sutton and Williams, 1970), and in Moore's (1973b) study of the limits to both component frequency identification and the audibility of residue PAGE 11-2 pitch. The only evidence from studies of tonal signals which favours a periodicity theory is that from studies of the effects on residue pitch of the relative phases of the components (Ritsma and Engel, 1964;Moore, 1977).…”

Section: Discussionmentioning

confidence: 99%

Models of the perception of the pitch of tonal complexes

Faulkner¹

1984

The Journal of the Acoustical Society of America

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Chapter 1 reviews the perception of pitch, with particular reference to the 'residue 1 pitch of complex tonal stimuli. The review considers those physical properties of sound which may be logically related to pitch, and, from a selective discussion of the published literature, attempts to critically evaluate the importance of the possible physical correlates of pitch. The possibility that simple transformations of sounds caused by auditory non-linearities may confound the supposedly independent manipulation of certain physical parameters is also considered.The Chapter provides a general discussion of the 'periodicity 1 theory due originally to Schouten (1938Schouten ( , 1940, the 'neural autocorrelation 1 theory described by Licklider (1959), and a 'spectral-pattern recognition' theory exemplified by two formal models developed by Wightman (1973b) and by Goldstein (1973).The reviewed studies indicate that residue pitch is primarily determined by the frequencies of resolved components rather than by periodicity, and it is concluded that the spectral-pattern theories are able to account for the majority of the available data.Chapter 2 discusses in detail the models proposed by Goldstein (1973) and Wightman (1973b). A general property of the spectral-pattern theories is that the residue pitch of a tonal complex can be considered as a random variable derived more or less directly from a weighted sum of a series of random variables representing internal estimates of the frequencies of ABSTRACT PAGE 2 the components of the signal. In contrast, the residue pitch predicted by Schouten's periodicity theory and by an unmodified version of Licklider's neural autocorrelation theory can be considered to be derived from a single random variable, this being an internal estimate of the period of an unanalysed residue. This distinction, together with the assumption that the components of a complex signal are well-resolved, leads to a psychophysically testable distinction between the spectral-pattern and the periodicity theories. According to the spectral-pattern models, Q"(f^ )/f0 , the relative standard deviation of the theoretical distribution of the residue pitch in the region of the fundamental frequency of a harmonic signal, should be smaller than the minimum of o-(f V-)/f : , the relative ______ v _____ J standard deviations of the theoretical internal distributions of estimates of the component frequencies. The periodicity theory leads to the reverse prediction. Predictions are also developed from the spectral-pattern models for the pitch probability density function (pdf) of the two two-component signals employed in the experiments described in the subsequent Chapters. If o"(f 'j )/f^ is known for each component of these signals, a computer simulation of these models can predict the areas of each mode of the multimodal pitch pdf. Estimates of the parameter cr(f^-)/fj from musical interval indent if ication tasks and from frequency discrimination for complex harmonic signals are discussed.

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Section: Discussionmentioning

confidence: 99%

Models of the perception of the pitch of tonal complexes

Faulkner¹

1984

The Journal of the Acoustical Society of America

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A New Approach to Model Pitch Perception Using Sparse Coding

Barzelay

Furst

Barak³

2017

PLoS Comput Biol

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Our acoustical environment abounds with repetitive sounds, some of which are related to pitch perception. It is still unknown how the auditory system, in processing these sounds, relates a physical stimulus and its percept. Since, in mammals, all auditory stimuli are conveyed into the nervous system through the auditory nerve (AN) fibers, a model should explain the perception of pitch as a function of this particular input. However, pitch perception is invariant to certain features of the physical stimulus. For example, a missing fundamental stimulus with resolved or unresolved harmonics, or a low and high-level amplitude stimulus with the same spectral content–these all give rise to the same percept of pitch. In contrast, the AN representations for these different stimuli are not invariant to these effects. In fact, due to saturation and non-linearity of both cochlear and inner hair cells responses, these differences are enhanced by the AN fibers. Thus there is a difficulty in explaining how pitch percept arises from the activity of the AN fibers. We introduce a novel approach for extracting pitch cues from the AN population activity for a given arbitrary stimulus. The method is based on a technique known as sparse coding (SC). It is the representation of pitch cues by a few spatiotemporal atoms (templates) from among a large set of possible ones (a dictionary). The amount of activity of each atom is represented by a non-zero coefficient, analogous to an active neuron. Such a technique has been successfully applied to other modalities, particularly vision. The model is composed of a cochlear model, an SC processing unit, and a harmonic sieve. We show that the model copes with different pitch phenomena: extracting resolved and non-resolved harmonics, missing fundamental pitches, stimuli with both high and low amplitudes, iterated rippled noises, and recorded musical instruments.

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Residue pitches from two-tone complexes

Cited by 2 publications

References 9 publications

Models of the perception of the pitch of tonal complexes

Models of the perception of the pitch of tonal complexes

A New Approach to Model Pitch Perception Using Sparse Coding

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