Virtual pitch and phase sensitivity of a computer model of the auditory periphery. I: Pitch identification

Meddis, Ray; Hewitt, Michael J.

doi:10.1121/1.400725

Cited by 429 publications

(312 citation statements)

References 57 publications

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“…Another possibility is that interval size is assessed in the early stages of auditory processing when partials are resolved by auditory filters. Several contemporary models of pitch perception propose that individual partials are resolved by auditory filters in the early stages of pitch processing followed by temporal analysis of neural firing patterns (e.g., Meddis & Hewitt, 1991;Moore, 1997;Patterson et al, 1992; see also Terhardt, 1974). It is possible that processes involved in determining the phenomenal size of a melodic interval may not only operate on the output from such pitch mechanisms but also be engaged at the early stages of pitch processing, reflecting a comparison of energy across all of the resolved partials.…”

Section: Discussionmentioning

confidence: 99%

An interval size illusion: The influence of timbre on the perceived size of melodic intervals

Russo

Thompson

2005

Perception & Psychophysics

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Section: Discussionmentioning

confidence: 99%

An interval size illusion: The influence of timbre on the perceived size of melodic intervals

Russo

Thompson

2005

Perception & Psychophysics

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“…This has led some authors to develop hybrid pitch models based on the use of both place and temporal information (16,79); these models assume that information from low and high harmonics is combined. However, the clearest pitch is heard when low harmonics are present (80,81), and the ability to detect changes in repetition rate is best when low harmonics are present (82,83).…”

Section: Coding Of Periodicity and Pitch Of Complex Tonesmentioning

confidence: 99%

Coding of Sounds in the Auditory System and Its Relevance to Signal Processing and Coding in Cochlear Implants

Moore

2003

Otology & Neurotology

146

127

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Objective: To review how the properties of sounds are "coded" in the normal auditory system and to discuss the extent to which cochlear implants can and do represent these codes. Data Sources: Data are taken from published studies of the response of the cochlea and auditory nerve to simple and complex stimuli, in both the normal and the electrically stimulated ear. Review Content: The review describes: 1) the coding in the normal auditory system of overall level (which partly determines perceived loudness), spectral shape (which partly determines perceived timbre and the identity of speech sounds), periodicity (which partly determines pitch), and sound location; 2) the role of the active mechanism in the cochlea, and particularly the fast-acting compression associated with that mechanism; 3) the neural response patterns evoked by cochlear implants; and 4) how the response patterns evoked by implants differ from those observed in the normal auditory system in response to sound. A series of specific issues is then discussed, including: 1) how to compensate for the loss of cochlear compression; 2) the effective number of independent channels in a normal ear and in cochlear implantees; 3) the importance of independence of responses across neurons; 4) the stochastic nature of normal neural responses; 5) the possible role of across-channel coincidence detection; and 6) potential benefits of binaural implantation. Conclusions: Current cochlear implants do not adequately reproduce several aspects of the neural coding of sound in the normal auditory system. Improved electrode arrays and coding systems may lead to improved coding and, it is hoped, to better performance.

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“…An autocorrelogram based on all-order interspike intervals has been used in various studies of auditory responses, such as modeling studies of the neural representation of pitch (Licklider 1951;Meddis and Hewitt 1991). The SAC is constructed by including the intervals across repetitions but excluding the intervals within each spike train.…”

Section: Correlation Indexmentioning

confidence: 99%

Statistical Analyses of Temporal Information in Auditory Brainstem Responses to Tones in Noise: Correlation Index and Spike-distance Metric

Gai

Carney

2008

JARO

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Gai and Carney (J Neurophysiol 96:2451(J Neurophysiol 96: -2464(J Neurophysiol 96: , 2006 previously explored the detection of tones in noise based on responses in the anteroventral cochlear nucleus; that study focused on temporal information in discharge reliability and analyses of neural responses related to the fine structure or envelope of the stimulus. Two additional temporal approaches, the correlation index (Joris et al., Hearing Res 216-217:19-30, 2006) and the spike-distance metric (Victor and Purpura, J Neurophysiol 76:1310-1326 Netw Comput Neural Syst 8:127-164, 1997), are tested in the present study. Trends in the correlation index as a function of stimulus level are similar to those of the synchronization coefficient (also called the vector strength) when the tone is presented alone. However, the present study found that trends in the correlation index did not agree with those of the synchronization coefficient for tones presented with relatively high-level background noise. Instead, trends in the correlation index generally agreed with those of the temporal reliability metric discussed in Gai and Carney (J Neurophysiol 96:2451(J Neurophysiol 96: -2464(J Neurophysiol 96: , 2006; that is, the correlation index decreased with increased tone level in the presence of relatively high-level background noise. The spike-distance metric, which was based on absolute spike times or on interspike intervals, was compared to the temporal measures described above, which were generally based on relative spike times. The results confirm that the spike-distance metric is not an optimal temporal metric. In addition, absolute spike times of primarylike responses generally contained much less temporal information than absolute spike times of chopper response types. The present study highlights the importance of relative spike-timing information as characterized by traditional and novel temporal measures.

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Virtual pitch and phase sensitivity of a computer model of the auditory periphery. I: Pitch identification

Cited by 429 publications

References 57 publications

An interval size illusion: The influence of timbre on the perceived size of melodic intervals

An interval size illusion: The influence of timbre on the perceived size of melodic intervals

Coding of Sounds in the Auditory System and Its Relevance to Signal Processing and Coding in Cochlear Implants

Statistical Analyses of Temporal Information in Auditory Brainstem Responses to Tones in Noise: Correlation Index and Spike-distance Metric

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