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Journal articles on the topic 'Pitch'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Tajika, Tsuyoshi, Noboru Oya, Tsuyoshi Ichinose, Daisuke Shimoyama, Tsuyoshi Sasaki, Takanori Hamano, Hitoshi Shitara, et al. "Relation between grip and pinch strength and pitch type in high school pitchers with and without elbow symptoms." Journal of Orthopaedic Surgery 28, no. 1 (January 1, 2020): 230949901989074. http://dx.doi.org/10.1177/2309499019890743.

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Objective: Gripping and pinching a ball is a fundamentally important part of the kinetic chain for throwing baseball pitches of various types. This study of high school pitchers was conducted to assess the association between grip and pinch strength, the pitch type, and the history of elbow symptoms. Methods: We examined 133 high school baseball pitchers, all of whom had completed a self-administered questionnaire including items related to pitch type throwing ratios, the age at starting each pitch type, and throwing-related elbow joint pain sustained during the prior 3 years. We measured grip strength and the bilateral side tip, key, and palmar pinch strengths. Comparisons were made between the participants with and without an elbow symptom history to assess the grip and each pinch strength, throwing ratio of pitch type, and the age at starting to throw each pitch type. Results: Pitchers with an elbow symptom history exhibited less difference between the grip strength on the throwing side than those with no elbow symptom history ( p = 0.04). No difference was found between participants with and without an elbow symptom history in terms of pinch strength, the throwing ratios of pitch types, or the age at starting to throw pitches of each type. Positive significant association was found between pinch strength on the pitching side and the forkball and screwball throwing ratio ( r = 0.27, p = 0.002). Conclusion: Grip strength might influence high school baseball pitcher elbow conditions. The frequency of certain pitch types might develop pinch strength in high school baseball pitchers.

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2

Portney, Daniel Aaron, Lucas T. Buchler, Jake Michael Lazaroff, Stephen Gryzlo, and Matthew Saltzman. "Release Location in a Risk Factor for Ulnar Collateral Ligament Reconstruction in Major League Baseball Pitchers." Orthopaedic Journal of Sports Medicine 6, no. 7_suppl4 (July 1, 2018): 2325967118S0016. http://dx.doi.org/10.1177/2325967118s00164.

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Objectives: Medial ulnar collateral ligament reconstruction (UCLR) is a common procedure performed for Major League Baseball (MLB) pitchers. The etiology of UCL injury is complex and not entirely understood. The purpose of this study was to use publically available pitch tracking technology (PITCHf/x) to compare the pre-injury throwing mechanics of MLB pitchers who require UCL reconstructive surgery with those of pitchers who have never undergone UCL reconstruction. Methods: Pitch tracking and demographic data on MLB pitchers who had undergone UCL reconstruction between the 2010 and 2017 seasons was gathered. Pitchers were excluded if they did not throw 100 total pitches in each of the three years prior to surgery. Furthermore, only pitch types that a given pitcher utilized more than 25 times in each of the three years prior to surgery were included for individual analysis. Pitch type, release location, and velocity were compared between the UCL reconstructive surgery cohort and a matched-control cohort. Results: The average pitch release location for pitchers who required UCL reconstruction was more lateral in the two years immediately preceding surgery than the control cohort (p=0.001 and p=0.023). Furthermore, a time-based comparison between the year immediately preceding surgery and two years prior showed a more lateral release immediately prior to surgery (p=0.036). Pitchers who required UCL reconstruction throw similar rates of fastballs as the control cohort and the average pitch velocity and fastball velocity were similar between the UCL group and the control group. The control pitchers displayed a significant decrease in average pitch velocity (p=0.005) and average fastball velocity (p=0.012) over the period of the study. Conclusion: Pitch tracking indicates pitch selection and pitch velocity are similar before that the average release point is more lateral preceding UCL reconstruction as compared to the control cohort suggesting that pitch release location might be an independent risk factor for UCL injury and reconstruction. On the other hand, pitch selection and pitch velocity are similar between these cohorts. Powerful technology including PITCHf/x allows for accurate monitoring of factors such as arm position and could potentially be used to identify pitchers at risk for UCL rupture. [Figure: see text]

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3

Deshpande, Sameer K., and Abraham Wyner. "A hierarchical Bayesian model of pitch framing." Journal of Quantitative Analysis in Sports 13, no. 3 (September 26, 2017): 95–112. http://dx.doi.org/10.1515/jqas-2017-0027.

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Abstract Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike). Especially in the last 3 years, there has been an explosion of interest in the “art of pitch framing” in the popular press as well as signs that teams are considering framing when making roster decisions. We introduce a Bayesian hierarchical model to estimate each umpire’s probability of calling a strike, adjusting for the pitch participants, pitch location, and contextual information like the count. Using our model, we can estimate each catcher’s effect on an umpire’s chance of calling a strike. We are then able translate these estimated effects into average runs saved across a season. We also introduce a new metric, analogous to Jensen, Shirley, and Wyner’s Spatially Aggregate Fielding Evaluation metric, which provides a more honest assessment of the impact of framing.

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4

Moore, Robert E., Casie Keaton, and Christopher Watts. "Role of Pitch Memory in Pitch Matching and Pitch Discrimination." ASHA Leader 10, no. 10 (August 2005): 4. http://dx.doi.org/10.1044/leader.ftr1.10102005.4.

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5

Cariani, P. A., and B. Delgutte. "Neural correlates of the pitch of complex tones. II. Pitch shift, pitch ambiguity, phase invariance, pitch circularity, rate pitch, and the dominance region for pitch." Journal of Neurophysiology 76, no. 3 (September 1, 1996): 1717–34. http://dx.doi.org/10.1152/jn.1996.76.3.1717.

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1. The neural correlates of low pitches produced by complex tones were studied by analyzing temporal discharge patterns of auditory nerve fibers in Dial-anesthetized cats. In the previous paper it was observed that, for harmonic stimuli, the most frequent interspike interval present in the population of auditory nerve fibers always corresponded to the perceived pitch (predominant interval hypothesis). The fraction of these most frequent intervals relative to the total number of intervals qualitatively corresponded to strength (salience) of the low pitches that are heard. 2. This paper addresses the neural correlates of stimuli that produce more complex patterns of pitch judgments, such as shifts in pitch and multiple pitches. Correlates of pitch shift and pitch ambiguity were investigated with the use of harmonic and inharmonic amplitude-modulated (AM) tones varying either in carrier frequency or modulation frequency. Pitches estimated from the pooled interval distributions showed shifts corresponding to "the first effect of pitch shift" (de Boer's rule) that is observed psychophysically. Pooled interval distributions in response to inharmonic stimulus segments showed multiple maxima corresponding to the multiple pitches heard by human listeners (pitch ambiguity). 3. AM and quasi-frequency-modulated tones with low carrier frequencies produce very similar patterns of pitch judgments, despite great differences in their phase spectra and waveform envelopes. Pitches estimated from pooled interval distributions were remarkably similar for the two kinds of stimuli, consistent with the psychophysically observed phase invariance of pitches produced by sets of low-frequency components. 4. Trains of clicks having uniform and alternating polarities were used to investigate the relation between pitches associated with periodicity and those associated with click rate. For unipolar click trains, where periodicity and rate coincide, physiologically estimated pitches closely follow the fundamental period. This corresponds to the pitch at the fundamental frequency (F0) that is heard. For alternating click trains, where rate and periodicity do not coincide, physiologically estimated pitches always closely followed the fundamental period. Although these pitch estimates corresponded to periodicity pitches that are heard for F0s > 150 Hz, they did not correspond to the rate pitches that are heard for F0s < 150 Hz. The predominant interval hypothesis thus failed to predict rate pitch. 5. When alternating-polarity click trains are high-pass filtered, rate pitches are strengthened and can also be heard at F0s > 150 Hz. Pitches for high-pass-filtered alternating click trains were estimated from pooled responses of fibers with characteristic frequencies (CFs) > 2 kHz. Roughly equal numbers of intervals at 1/rate and 1/F0 were found for all F0s studied, from 80 to 160 Hz, producing pitch estimates consistent with the rate pitches that are heard after high-pass filtering. The existence region for rate pitch also coincided with the presence of clear periodicities related to the click rate in pooled peristimulus time histograms. These periodicities were strongest for ensembles of fibers with CFs > 2 kHz, where there is widespread synchrony of discharges across many fibers. 6. The "dominance region for pitch" was studied with the use of two harmonic complexes consisting of harmonics 3-5 of one F0 and harmonics 6-12 of another fundamental 20% higher in frequency. When the complexes were presented individually, pitch estimates were always close to the fundamental of the complex. When the complexes were presented concurrently, pitch estimates always followed the fundamental of harmonics 3-5 for F0s of 150-480 Hz. For F0s of 125-150 Hz, pitch estimates followed one or the other fundamental, and for F0s < 125 Hz, pitch estimates followed the fundamental of harmonics 6-12. (ABSTRACT TRUNCATED)

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6

Anta, J. Fernando. "Pitch." Music Perception 32, no. 4 (April 1, 2015): 413–33. http://dx.doi.org/10.1525/mp.2015.32.4.413.

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Two experiments investigated the role of pitch-related information in tonality induction. In both experiments, participants were asked to: 1) identify (sing) the tonic of either an original sequence of tones or a distorted version in which pitch class distribution was preserved but pitch class ordering, pitch contour, and/or pitch proximity were altered; and 2) rate how confident they were in the tonic they identified. In Experiment 2, the sequences were presented with an isochronous rhythm, in order to eliminate the potential confounding effects of time-related information. The results of both experiments showed that participants’ ability to identify the tonic of the sequences, as well as their confidence in the tonic they identified, decreased when pitch class ordering was distorted, and also when pitch proximity was reduced. This suggests that tonality induction not only involves the identification of abstract pitch class structures, but it also acts as a pattern-matching process.

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7

McDermott, Josh H., Andriana J. Lehr, and Andrew J. Oxenham. "Is Relative Pitch Specific to Pitch?" Psychological Science 19, no. 12 (December 2008): 1263–71. http://dx.doi.org/10.1111/j.1467-9280.2008.02235.x.

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Melodies, speech, and other stimuli that vary in pitch are processed largely in terms of the relative pitch differences between sounds. Relative representations permit recognition of pitch patterns despite variations in overall pitch level between instruments or speakers. A key component of relative pitch is the sequence of pitch increases and decreases from note to note, known as the melodic contour. Here we report that contour representations are also produced by patterns in loudness and brightness (an aspect of timbre). The representations of contours in different dimensions evidently have much in common, as contours in one dimension can be readily recognized in other dimensions. Moreover, contours in loudness and brightness are nearly as useful as pitch contours for recognizing familiar melodies that are normally conveyed via pitch. Our results indicate that relative representations via contour extraction are a general feature of the auditory system, and may have a common central locus.

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8

Peters, Robert W., and Joseph W. Hall. "Conditioned pitch change and pitch adaptation." Journal of the Acoustical Society of America 79, S1 (May 1986): S80. http://dx.doi.org/10.1121/1.2023407.

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9

Lucia, Andrew, Christopher Lee, and Matthew Lake. "Pitch to Rhythm ∷ Rhythm to Pitch." Leonardo Music Journal 20 (December 2010): 97. http://dx.doi.org/10.1162/lmj_a_00025.

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10

Moore, Robert E., Casie Keaton, and Christopher Watts. "The Role of Pitch Memory in Pitch Discrimination and Pitch Matching." Journal of Voice 21, no. 5 (September 2007): 560–67. http://dx.doi.org/10.1016/j.jvoice.2006.04.004.

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11

Yao, Qi, Ying Xue Yao, Liang Zhou, and S. Y. Zheng. "Numerical Study of Pitch Angle on H-Type Vertical Axis Wind Turbine." Key Engineering Materials 499 (January 2012): 259–64. http://dx.doi.org/10.4028/www.scientific.net/kem.499.259.

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This paper presents a simulation study of an H-type vertical axis wind turbine. Two dimensional CFD model using sliding mesh technique was generated to help understand aerodynamics performance of this wind turbine. The effect of the pith angle on H-type vertical axis wind turbine was studied based on the computational model. As a result, this wind turbine could get the maximum power coefficient when pitch angle adjusted to a suited angle, furthermore, the effects of pitch angle and azimuth angle on single blade were investigated. The results will provide theoretical supports on study of variable pitch of wind turbine.

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12

Miyazaki, Ken’ichi. "Musical pitch identification by absolute pitch possessors." Perception & Psychophysics 44, no. 6 (November 1988): 501–12. http://dx.doi.org/10.3758/bf03207484.

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13

Van Borsel, John, Jana Vandaele, and Paul Corthals. "Pitch and Pitch Variation in Lesbian Women." Journal of Voice 27, no. 5 (September 2013): 656.e13–656.e16. http://dx.doi.org/10.1016/j.jvoice.2013.04.008.

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14

Baranek, R., and F. Solc. "Hexacopter Pitch Estimator for a Pitch Stabilizer." IFAC Proceedings Volumes 46, no. 28 (2013): 326–29. http://dx.doi.org/10.3182/20130925-3-cz-3023.00021.

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15

Ikeda, Saeko. "Precision of pitch memory and accuracy of pitch labeling in absolute pitch perception." Proceedings of the Annual Convention of the Japanese Psychological Association 78 (September 10, 2014): 1PM—1–066–1PM—1–066. http://dx.doi.org/10.4992/pacjpa.78.0_1pm-1-066.

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16

Ikeda, Saeko. "Precision of pitch memory and accuracy of pitch labeling in absolute pitch perception." Journal of Human Environmental Studies 12, no. 2 (2014): 161–68. http://dx.doi.org/10.4189/shes.12.161.

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17

Smith, Nicholas A., and Mark A. Schmuckler. "Dial A440 for absolute pitch: Absolute pitch memory by non-absolute pitch possessors." Journal of the Acoustical Society of America 123, no. 4 (April 2008): EL77—EL84. http://dx.doi.org/10.1121/1.2896106.

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18

Cariani, P. A., and B. Delgutte. "Neural correlates of the pitch of complex tones. I. Pitch and pitch salience." Journal of Neurophysiology 76, no. 3 (September 1, 1996): 1698–716. http://dx.doi.org/10.1152/jn.1996.76.3.1698.

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1. The temporal discharge patterns of auditory nerve fibers in Dial-anesthetized cats were studied in response to periodic complex acoustic waveforms that evoke pitches at their fundamental frequencies. Single-formant vowels, amplitude-modulated (AM) and quasi-frequency-modulated tones. AM noise, click trains, and other complex tones were utilized. Distributions of intervals between successive spikes ("1st-order intervals") and between both successive and nonsuccessive spikes ("all-order intervals") were computed from spike trains. Intervals from many fibers were pooled to estimate interspike interval distributions for the entire auditory nerve. Properties of these "pooled interspike interval distributions," such as the positions of interval peaks and their relative heights, were examined for correspondence to the psychophysical data on pitch frequency and pitch salience. 2. For a diverse set of complex stimuli and levels, the most frequent all-order interspike interval present in the pooled distribution corresponded to the pitch heard in psychophysical experiments. Pitch estimates based on pooled interval distributions (30-85 fibers, 100 stimulus presentations per fiber) were highly accurate (within 1%) for harmonic stimuli that produce strong pitches at 60 dB SPL. 3. Although the most frequent intervals in pooled all-order interval distributions were very stable with respect to sound intensity level (40, 60, and 80 dB total SPL), this was not necessarily the case for first-order interval distributions. Because the low pitches of complex tones are largely invariant with respect to level, pitches estimated from all-order interval distributions correspond better to perception. 4. Spectrally diverse stimuli that evoke similar low pitches produce pooled interval distributions with similar most-frequent intervals. This suggests that the pitch equivalence of these different stimuli could result from central auditory processing mechanisms that analyze interspike interval patterns. 5. Complex stimuli that evoke strong or "salient" pitches produce pooled interval distributions with high peak-to-mean ratios. Those stimuli that evoke weak pitches produce pooled interval distributions with low peak-to-mean ratios. 6. Pooled interspike interval distributions for stimuli consisting of low-frequency components generally resembled the short-time auto-correlation function of stimulus waveforms. Pooled interval distributions for stimuli consisting of high-frequency components resembled the short-time autocorrelation function of the waveform envelope. 7. Interval distributions in populations of neurons constitute a general, distributed means of encoding, transmitting, and representing information. Existence of a central processor capable of analyzing these interval patterns could provide a unified explanation for many different aspects of pitch perception.

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19

Neher, Jon O. "Perfect pitch." Evidence-Based Practice 16, no. 6 (June 2013): 3. http://dx.doi.org/10.1097/01.ebp.0000540391.93607.f5.

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20

Rosenberg, Ruth E. "Perfect Pitch." Journal of Popular Music Studies 33, no. 1 (March 1, 2021): 137–54. http://dx.doi.org/10.1525/jpms.2021.33.1.137.

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432 Hz music is a relatively recent internet-based phenomenon that has attracted listeners and musicians from all parts of the world. Increasingly connected via social media, listeners in this subculture do not necessarily share the same musical tastes or backgrounds. Rather, they have in common a belief that music tuned to the standard pitch of A-440 Hz is in some sense “out of tune” with nature or humanity. Alternatively, they prefer (and in some cases promote and advocate for) music that is tuned to a slightly lower, A-432 Hz standard. This preference is, for many, connected to beliefs that the A-432 Hz tuning reference can be physically, psychologically, and even spiritually beneficial. This article examines the promise of—and skepticism towards—the concept of “frequency” that is at the center of the 432 Hz phenomenon. It draws from research into some of the common historical, scientific, and conspiratorial claims made by 432 Hz advocates, as well as from qualitative data collected from dedicated 432 Hz listeners. After exploring the listening practices and media engagement of 432 Hz proponents, the article asks how the rise of 432 Hz music might relate to other recent and emerging forms of music consumption, the affective marketing of sound, and the management of personal sonic space.

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21

Yost, W. A. "Pitch perception." Attention, Perception & Psychophysics 71, no. 8 (November 1, 2009): 1701–15. http://dx.doi.org/10.3758/app.71.8.1701.

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22

Driver, Carolyn. "Fever pitch." Nursing Standard 17, no. 4 (October 9, 2002): 15. http://dx.doi.org/10.7748/ns.17.4.15.s35.

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23

Bates, Jane. "Sales pitch." Nursing Standard 21, no. 16 (January 2, 2007): 28. http://dx.doi.org/10.7748/ns.21.16.28.s45.

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24

Hanning, James. "Pitch invasion." British Journalism Review 32, no. 2 (May 27, 2021): 16–17. http://dx.doi.org/10.1177/09564748211020945.

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25

Bates, Jane. "Fever pitch." Nursing Standard 24, no. 4 (September 30, 2009): 27. http://dx.doi.org/10.7748/ns.24.4.27.s31.

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26

Slonimsky, Nicolas. "Perfect Pitch." Grand Street 6, no. 1 (1986): 160. http://dx.doi.org/10.2307/25006936.

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27

Betts, Robert Brenton. "Off Pitch." Musical Times 137, no. 1842 (August 1996): 3. http://dx.doi.org/10.2307/1003955.

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28

Kenway, Bruno, Yu Chuen Tam, Zebunnisa Vanat, Frances Harris, Roger Gray, John Birchall, Robert Carlyon, and Patrick Axon. "Pitch Discrimination." Otology & Neurotology 36, no. 9 (September 2015): 1472–79. http://dx.doi.org/10.1097/mao.0000000000000845.

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29

Ledford, Heidi. "Fever pitch." Nature 450, no. 7170 (November 2007): 600–601. http://dx.doi.org/10.1038/450600a.

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30

Wennerstrom, Ann. "Rich pitch." Pragmatics and Cognition 19, no. 2 (August 10, 2011): 310–32. http://dx.doi.org/10.1075/pc.19.2.07wen.

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This paper argues that intonation contributes to the humorous meaning of a certain class of jokes. Examples of both canned and spontaneous jokes show that two intonation patterns, the intonation of contrast, or “L+H* pitch accent”, and the intonation of given information, or “deaccent”, can contribute to a humorous effect. Both of these patterns act as cohesive devises in discourse: they trigger a mental search in the mind of a hearer for a cohesive tie that may not be obvious from the lexicogrammatical structure alone. A punch line effect is created if this search yields an unexpected incongruity between the hearer’s initial mental model of the joke discourse and a humorous alternative. The hearer must shift his “script” (Raskin 1984) of the discourse in an unexpected way. To the extent that intonation facilitates processing by directing attention to particular elements in the information structure of the discourse (Chafe 1994), the processing of jokes depends in part on their intonation. The implications of this premise for the processing of humorous texts will be discussed for the two intonation patterns in question. It is argued that intonation analysis can lead to a broader understanding of cognitive processes and structures.

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31

Radius, Susan M., and Kim Tran. "Perfect Pitch." Health Promotion Practice 13, no. 1 (January 2012): 5–9. http://dx.doi.org/10.1177/1524839911432933.

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32

McNeill, Kristopher. "Elevator pitch." Environmental Science: Processes & Impacts 18, no. 3 (2016): 304–5. http://dx.doi.org/10.1039/c6em90008a.

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33

Segerman, Ephraim. "Praetorius's pitch?" Early Music 13, no. 2 (May 1985): 261–66. http://dx.doi.org/10.1093/earlyj/13.2.261.

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34

Harries, Judith. "Pitch perfect." Early Years Educator 21, no. 11 (March 2, 2020): S6—S7. http://dx.doi.org/10.12968/eyed.2020.21.11.s6.

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35

Werner, Lynne, Angela Garinis, Bonnie Lau, and Louise Yeager. "Pitch perception." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2540. http://dx.doi.org/10.1121/1.3588445.

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36

Baker, D. "Bright Pitch." Literary Imagination 10, no. 3 (June 3, 2008): 296–98. http://dx.doi.org/10.1093/litimag/imn017.

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37

Takeuchi, Annie H., and Stewart H. Hulse. "Absolute pitch." Psychological Bulletin 113, no. 2 (1993): 345–61. http://dx.doi.org/10.1037/0033-2909.113.2.345.

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38

Li, Shuai. "Pitch imperfect." Science 360, no. 6389 (May 10, 2018): 678. http://dx.doi.org/10.1126/science.360.6389.678.

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39

Dougherty, Robert F., Max S. Cynader, Bruce H. Bjornson, Dorothy Edgell, and Deborah E. Giaschi. "Dichotic pitch." NeuroReport 9, no. 13 (September 1998): 3001–5. http://dx.doi.org/10.1097/00001756-199809140-00015.

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40

Pound, Linda. "Pitch in!" Nursery World 2018, no. 4 (February 19, 2018): 16–18. http://dx.doi.org/10.12968/nuwa.2018.4.16.

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41

Gibbs, Marilyn J. "The Pitch." Quest 41, no. 2 (August 1989): 160. http://dx.doi.org/10.1080/00336297.1989.10483959.

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42

Williams, Peter, and Matin Durrani. "Pitch perfect." Physics World 31, no. 7 (July 2018): 19. http://dx.doi.org/10.1088/2058-7058/31/7/28.

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43

Johanse-Berg, H. "Perfect pitch." Trends in Cognitive Sciences 5, no. 4 (April 2001): 138. http://dx.doi.org/10.1016/s1364-6613(00)01642-9.

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44

Portigal, Steve, and Julie Norvaisas. "Elevator pitch." Interactions 18, no. 4 (July 2011): 14–16. http://dx.doi.org/10.1145/1978822.1978826.

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45

Dent, Thomas. "Imperfect pitch." New Scientist 196, no. 2629 (November 2007): 27. http://dx.doi.org/10.1016/s0262-4079(07)62844-9.

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46

Oxenham, A. J. "Pitch Perception." Journal of Neuroscience 32, no. 39 (September 26, 2012): 13335–38. http://dx.doi.org/10.1523/jneurosci.3815-12.2012.

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47

Gittings, John. "Fever pitch." Index on Censorship 29, no. 3 (May 2000): 123–27. http://dx.doi.org/10.1080/03064220008536731.

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48

Ditmer, Nancy E. "President’s Pitch." Music Educators Journal 99, no. 1 (September 2012): 5. http://dx.doi.org/10.1177/0027432112452230.

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49

Ditmer, Nancy E. "President’s Pitch." Music Educators Journal 99, no. 2 (December 2012): 12. http://dx.doi.org/10.1177/0027432112464027.

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50

Ditmer, Nancy E. "President’s Pitch." Music Educators Journal 99, no. 3 (March 2013): 9. http://dx.doi.org/10.1177/0027432112472473.

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