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      A Dynamical Model of Pitch Memory Provides an Improved Basis for Implied Harmony Estimation

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          Tonal melody can imply vertical harmony through a sequence of tones. Current methods for automatic chord estimation commonly use chroma-based features extracted from audio signals. However, the implied harmony of unaccompanied melodies can be difficult to estimate on the basis of chroma content in the presence of frequent nonchord tones. Here we present a novel approach to automatic chord estimation based on the human perception of pitch sequences. We use cohesion and inhibition between pitches in auditory short-term memory to differentiate chord tones and nonchord tones in tonal melodies. We model short-term pitch memory as a gradient frequency neural network, which is a biologically realistic model of auditory neural processing. The model is a dynamical system consisting of a network of tonotopically tuned nonlinear oscillators driven by audio signals. The oscillators interact with each other through nonlinear resonance and lateral inhibition, and the pattern of oscillatory traces emerging from the interactions is taken as a measure of pitch salience. We test the model with a collection of unaccompanied tonal melodies to evaluate it as a feature extractor for chord estimation. We show that chord tones are selectively enhanced in the response of the model, thereby increasing the accuracy of implied harmony estimation. We also find that, like other existing features for chord estimation, the performance of the model can be improved by using segmented input signals. We discuss possible ways to expand the present model into a full chord estimation system within the dynamical systems framework.

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          Most cited references 51

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          Auditory Sensitivity Provided by Self-tuned Critical Oscillations of Hair Cells

          We introduce the concept of self-tuned criticality as a general mechanism for signal detection in sensory systems. In the case of hearing, we argue that active amplification of faint sounds is provided by a dynamical system which is maintained at the threshold of an oscillatory instability. This concept can account for the exquisite sensitivity of the auditory system and its wide dynamic range, as well as its capacity to respond selectively to different frequencies. A specific model of sound detection by the hair cells of the inner ear is discussed. We show that a collection of motor proteins within a hair bundle can generate oscillations at a frequency which depends on the elastic properties of the bundle. Simple variation of bundle geometry gives rise to hair cells with characteristic frequencies which span the range of audibility. Tension-gated transduction channels, which primarily serve to detect the motion of a hair bundle, also tune each cell by admitting ions which regulate the motor protein activity. By controlling the bundle's propensity to oscillate, this feedback automatically maintains the system in the operating regime where it is most sensitive to sinusoidal stimuli. The model explains how hair cells can detect sounds which carry less energy than the background noise.
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            Implicit learning and acquisition of music.

            Implicit learning is a core process for the acquisition of a complex, rule-based environment from mere interaction, such as motor action, skill acquisition, or language. A body of evidence suggests that implicit knowledge governs music acquisition and perception in nonmusicians and musicians, and that both expert and nonexpert participants acquire complex melodic, harmonic, and other features from mere exposure. While current findings and computational modeling largely support the learning of chunks, some results indicate learning of more complex structures. Despite the body of evidence, more research is required to support the cross-cultural validity of implicit learning and to show that core and more complex music theoretical features are acquired implicitly. Copyright © 2012 Cognitive Science Society, Inc.
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              Anchoring effects in music: The resolution of dissonance

               J.J. Bharucha (1984)

                Author and article information

                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                04 May 2017
                : 8
                1Department of Psychological Sciences, University of Connecticut Storrs, CT, USA
                2Oscilloscape LLC East Hartford, CT, USA
                Author notes

                Edited by: Naresh N. Vempala, Ryerson University, Canada

                Reviewed by: Dipanjan Roy, Allahabad University, India; Jane Elizabeth Bednarz, Texa A&M University-Commerce, USA

                *Correspondence: Ji Chul Kim jichulkim21@

                This article was submitted to Cognition, a section of the journal Frontiers in Psychology

                Copyright © 2017 Kim.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 6, Tables: 0, Equations: 4, References: 56, Pages: 10, Words: 7752
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: BCS-1027761
                Funded by: Air Force Office of Scientific Research 10.13039/100000181
                Award ID: FA9550-12-10388
                Original Research


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