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A Scale for the Measurement of the Psychological Magnitude Pitch
Abstract
A subjective scale for the measurement of pitch was constructed from determinations of the half-value of pitches at various frequencies. This scale differs from both the musical scale and the frequency scale, neither of which is subjective. Five observers fractionated tones of 10 different frequencies at a loudness level of 60 db. From these fractionations a numerical scale was constructed which is proportional to the perceived magnitude of subjective pitch. In numbering the scale the 1000-cycle tone was assigned the pitch of 1000 subjective units (mels). The close agreement of the pitch scale with an integration of the differential thresholds (DL's) shows that, unlike the DL's for loudness, all DL's for pitch are of uniform subjective magnitude. The agreement further implies that pitch and differential sensitivity to pitch are both rectilinear functions of extent on the basilar membrane. The correspondence of the pitch scale and the experimentally determined location of the resonant areas of the basilar membrane suggests that, in cutting a pitch in half, the observer adjusts the tone until it stimulates a position half-way from the original locus to the apical end of the membrane. Measurement of the subjective size of musical intervals (such as octaves) in terms of the pitch scale shows that the intervals become larger as the frequency of the mid-point of the interval increases (except in the two highest audible octaves). This result confirms earlier judgments as to the relative size of octaves in different parts of the frequency range.
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“The distance between the four orbits and the P (or the NP) vowel was equated in psychophysical terms.The metric used to equate the psychophysical distance of the orbits from P and NP was the mel scale (S. S. Stevens, Volkmann, & Newman, 1937).”
From Human adults and human infants show a “perceptual magnet effect” for the prototypes of speech categories, monkeys do not (1991) · cites this paper as S. S. Stevens, Volkmann, & Newman, 1937
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González, J., & Crespo, T. (2001) · RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya) · Cited by 1
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Rajput, N., & Nanavati, A. A. (2012) · doi:10.1002/9781119961710.ch4 · Cited by 1
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Cofino, J. (2014) · doi:10.25148/etd.fi14071117 · Cited by 1
Vester, H., Hammerschmidt, K., Timme, M., et al. (2014) · arXiv (Cornell University) · Cited by 1
Durrieu, J.-L. (2010) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
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Radocy, R. E. (1977) · Psychology of Music · doi:10.1177/030573567752003 · Cited by 1
Lodder, S. S. (2009) · SUNScholar (Stellenbosch University) · Cited by 1
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Murray, J. C., & Cañamero, L. D. (2009) · IEEE Symposium on Artificial Life (ALIFE) · doi:10.1109/alife.2009.4937697 · Cited by 1
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MethodGeneralized multi-stream hidden Markov models.
Missaoui, O. (2010) · doi:10.18297/etd/990 · Cited by 1
Aesthetically driven design of network based multi-user instruments.
McKinney, C. (2014) · Bournemouth University Research Online (Bournemouth University) · Cited by 1
Wong, W. (1997) · Library and Archives Canada (Government of Canada) · Cited by 1
Descriptor control of sound transformations and mosaicing synthesis
Coleman, G. (2016) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1
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Abel, J. (2015) · cIRcle (University of British Columbia) · doi:10.14288/1.0166308 · Cited by 1
Toward a unified theory of psychophysics
McBride, R. (2013) · Figshare · doi:10.25949/19432487 · Cited by 1
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Brandt, J. F. (1976) · Elsevier eBooks · doi:10.1016/b978-0-12-437150-7.50019-1 · Cited by 1
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Whitfield, I. C. (1980) · Springer eBooks · doi:10.1007/978-94-009-9144-6_50 · Cited by 1
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Smit, P. (2011) · Aaltodoc (Aalto University) · Cited by 1
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Clark, N. A. (2012) · doi:10.31390/gradschool_dissertations.3723 · Cited by 1
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BackgroundHeelan, C., Lee, J.-H., O’Shea, R. T., Brandman, D. M., Truccolo, W., & Nurmikko, A. V. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/614248 · Cited by 1
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Govender, D. (2018) · Open University of Cape Town (University of Cape Town) · Cited by 1
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Alghamdi, M. S. (2019) · Digital Collections of Colorado (Colorado State University) · Cited by 1
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Bird, J. J., Faria, D. R., Ekárt, A., Premebida, C., & Ayrosa, P. P. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00659 · Cited by 6
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Alon, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.13997 · Cited by 1
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Agrawal, P. (2021) · Cited by 1
Pitch-Intensity Dependence and its Implications for Pitch Perception
Miyazaki, K.-I. (1978) · Institutional Repositories DataBase (IRDB) · Cited by 1
Krish, D. (2021) · Preprints.org · doi:10.20944/preprints202107.0014.v1 · Cited by 1
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Russo, F., & Thompson, W. F. (2021) · doi:10.32920/14637465.v1 · Cited by 1
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Skudrƶyk, E. (1954) · Springer eBooks · doi:10.1007/978-3-7091-5830-2_25
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Machado, A. F. (2013) · doi:10.11606/t.45.2013.tde-04062013-130812
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BackgroundBrodeur, S., & Rouat, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.5924
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BackgroundMethodPhung, T.-N., Do, H.-K., Tảo, N. V., & Thai, Q.-V. (2015) · Advances in Acoustics and Vibration · doi:10.1155/2015/170183
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Hsueh, C.-H., & Yu, T. (2012) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/2330784.2330968
Research pertaining to the singing voice 1925‐50
Hutton, C., & Ball, J. M. (1953) · Central States Speech Journal · doi:10.1080/10510975309362325
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de Medeiros, B. R., & Martins, M. V. M. (2014) · Alfa · doi:10.1590/s1981-57942014000100008
Vector quantization of speech signals using principal component analysis
Kohata, M., Sone, H., Echigo, H., et al. (1987) · Electronics and Communications in Japan (Part I Communications) · doi:10.1002/ecja.4410700502
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Richards, A. M. (1969) · The Journal of the Acoustical Society of America · doi:10.1121/1.1911588
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Choi, Y. H., Ban, S. M., Kim, K.-W., et al. (2015) · Phonetics and Speech Sciences · doi:10.13064/ksss.2015.7.1.003
“멜 스케일은 물리적인 음 높이와 청각 인지적인 음높이의 관계를 표현하는 것으로서, Stevens 등에 의해 명명되었다[11].”
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Wenxi, C. (1999) · The Journal of General Psychology · doi:10.1080/00221309909595361
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Chakroun, R., & Ayed, Y. B. (2012) · doi:10.1109/ssd.2012.6197955
“These coefficients are proposed to represent the cepstrum [3] by basing on a bench of filters following a scale of frequency which reports the human perception, called the Mel scale [13].”
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Rao, K. S., & Vuppala, A. K. (2013) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-03116-3_5
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Albertini, M. K., Li, K., & de Mello, R. F. (2008) · IEEE Asia-Pacific Services Computing Conference (APSCC) · doi:10.1109/apscc.2008.238
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Asari, H. (2007) · Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory)
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Roederer, J. G. (2008) · Springer eBooks · doi:10.1007/978-0-387-09474-8_2
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de Oliveira, L. C. (2013)
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Sinha, S., Talukdar, J., & Acharjee, P. B. (2012)
Sawant, A., Vasandani, B., Ochaney, M., et al. (2014)
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Cajas, D. F. G., Sepúlveda, A., & Serrano, M. A. P. (2014)
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Lagrange, M., Lafay, G., & Rossignol, M. (2014) · HAL (Le Centre pour la Communication Scientifique Directe)
Consonant–Vowel Recognition in the Presence of Coding and Background Noise
Rao, K. S., & Vuppala, A. K. (2013) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-03116-3_4
Aplicação do método de fusão para verificação de locutor independente de texto
da Silva, M. F. (2015)
Silva, D. F., & Batista, G. (2014) · Scientific Electronic Library Online (São Paulo Research Foundation, Latin American and Caribbean Center on Health Sciences Information, Conselho Nacional de Desenvolvimento Científico e Tecnológico)
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Şentürk, Z., & Salor, Ö. (2015) · doi:10.1109/eleco.2015.7394575
An Application of Calculated Consonance in Computer-Assisted Microtonal Music
Burleigh, I. (2013) · Libraries and Cultural Resources (University of Calgary) · doi:10.11575/prism/24833
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Song, Y. (2012) · OakTrust (Texas A&M University Libraries)
Esmaeili, M. M. (2013) · Open Collections · doi:10.14288/1.0073906
Mathématiques, É. D. D. (2013)
Methods of measurements in sensation and perception (II b)
KOYATSU, T. (1968) · The Japanese Journal of Ergonomics · doi:10.5100/jje.4.3_227
Brodeur, S., & Rouat, J. (2013) · Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)
Synthetic Speech Detection and Audio Steganography in VoIP Scenarios
Capolupo, D., & d’Amore, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-31960-5_13
“The MFCCs (Mel Frequency Cepstral Coefficients[1, 8]) are calculated for each single frame without knowing the next frame (frame by frame), therefore, it is very difficult to capture the correlations between frames, or the temporal characteristics.”
BackgroundTongue based hearing enhancement system
Cha, S. (2015) · Maynooth University ePrints and eTheses Archive (Maynooth University)
The Second Quarter of the Twentieth Century
Beyer, R. T. (1999) · Springer eBooks · doi:10.1007/978-1-4612-0517-3_8
Metadata
- Authors
- Stevens, S. S., Volkmann, J., & Newman, E. B.
- Affiliations
- Harvard University (US)
- Swarthmore College (US)
- ORCID
- Volkmann, J.
- Venue
- The Journal of the Acoustical Society of America
- Publisher
- Acoustical Society of America (ASA)
- Published
- 1937-01-01
- Volume
- 8
- Issue
- 3
- Pages
- 185-190
- ISSN
- 0001-4966 (ISSN-L)
- 1520-8524
- OpenAlex
- W2103869314
- Cited by
- 1,107
- References
- 0
- Citations by year
- 2026: 41 · 2025: 68 · 2024: 73 · 2023: 78 · 2022: 77 · 2021: 96 · 2020: 72 · 2019: 68 · 2018: 51 · 2017: 43 · 2016: 45 · 2015: 55 · 2014: 30 · 2013: 53 · 2012: 46
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of the Acoustical Society of America (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience and Music Perception
- Life Sciences › Neuroscience › Sensory Systems › Hearing, Cochlea, Tinnitus, Genetics
- Physical Sciences › Physics and Astronomy › Acoustics and Ultrasonics › Human auditory perception and evaluation
- Keywords
- Mel scale, basilar membrane, sound localization, musical intervals, frequency perception
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