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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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Morise, M., Genta, M., & Ozawa, K. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-67 · Cited by 6
“The mel scale [20] is one of the most popular scales that is a perceptual scale of pitches.”
BackgroundA domain-agnostic approach for opinion prediction on speech
Santos, P. B., Beinborn, L., & Gurevych, I. (2016) · TUbilio (Technical University of Darmstadt) · Cited by 6
Hearing triangles: Perceptual clarity, opacity, and symmetry of spectrotemporal sound shapes
Lembke, S.-A. (2018) · The Journal of the Acoustical Society of America · doi:10.1121/1.5048130 · Cited by 6
“Human perception is known to favor logarithmic, relative dependencies for both frequency (e.g., Attneave and Olson, 1971; Moore and Glasberg, 1983; Stevens et al., 1937) and amplitude (e.g., Fletcher and Munson, 1933).”
BackgroundAudio source separation to reduce sleeping partner sounds: a simulation study
Mordoh, V., & Zigel, Y. (2021) · Physiological Measurement · doi:10.1088/1361-6579/ac0592 · Cited by 6
L'oreille absolue: une oreille plus “fine” ?
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Vocal Aging Explained by Vocal Tract Modelling: 2008 JHU Summer Workshop Final Report 1
Beyerlein, P., Kholhatkar, V., Nöth, E., et al. (2008) · Cited by 6
Mental task classification using single-electrode brain computer interfaces
Hassib, M. (2012) · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · doi:10.18419/opus-2963 · Cited by 6
Asymmetries in vowel perception: Effects of formant convergence and category “goodness”
Masapollo, M., Polka, L., & Ménard, L. (2015) · The Journal of the Acoustical Society of America · doi:10.1121/1.4920678 · Cited by 6
Exploring spectro-temporal features in end-to-end convolutional neural networksPreprint
Robertson, S., Penn, G., & Wang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00072 · Cited by 6
“The h k are triangular windows with vertices derived by linearly sampling the Mel scale [31].”
MethodLi, J., Kang, D., Pei, W., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.06720 · Cited by 6
Beyond $L_p$ clipping: Equalization-based Psychoacoustic Attacks against ASRsPreprint
Abdullah, H., Rahman, M. S., Peeters, C., Gibson, C., García, W., Bindschaedler, V., Shrimpton, T., & Traynor, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.13250 · Cited by 8
“Studies in the field of human audio perception have resulted in widely familiar models of frequency perception; the Mel Scale Stevens et al. (1937) and the BARK Scale Zwicker (1961) 1 , the later of which we use in our masking frequency calculations described in Algorithm 1.”
MethodGenerative AI-based data augmentation for improved bioacoustic classification in noisy environments
Gibbons, A., King, E., Donohue, I., & Parnell, A. (2024) · arXiv · doi:10.48550/arxiv.2412.01530 · Cited by 6
Direct Speech Reconstruction from Sensorimotor Brain Activity with Optimized Deep Learning Models
Berezutskaya, J., Freudenburg, Z. V., Vansteensel, M. J., Aarnoutse, E. J., Ramsey, N. F., & van Gerven, M. A. J. (2022) · doi:10.1101/2022.08.02.502503 · Cited by 42
Comparing Representations for Audio Synthesis Using Generative Adversarial Networks
Nistal, J., Lattner, S., & Gaël Richard, A. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287799 · Cited by 5
“For example, deep autoregressive techniques working directly on raw audio [3], as well as on Mel-scaled spectrograms [4], currently yield state-of-the-art results in terms of quality.”
MethodLiu, S., Qiao, Z., Ma, T., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3570985 · Cited by 5
“The MFCC [28], [31] is a cepstral parameter extracted from the frequency domain using the Mel scale, which relates to frequency according to the following approximation: where F Mel ( f ) represents the frequency detection per Mel unit, and f is the actual frequency in Hertz.”
Chakrabarti, S., & De, P. (2025) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-025-00968-2 · Cited by 5
Age Classification Based on Voice Using Mel-Spectrogram and MFCC
Al-Maashani, T., Mendonça, I., & Aritsugi, M. (2023) · doi:10.1109/dsp58604.2023.10167887 · Cited by 5
“In point to the fact that Mel-scale is logarithmic above 1 kHz, and approximately linear below that [6].”
BackgroundLocal intelligence: time to learn from AI
Başarır, L., Çiçek, S., & Koç, M. (2024) · Architectural Science Review · doi:10.1080/00038628.2024.2333547 · Cited by 5
“Due to its close approximation to human perception of frequencies, the Mel frequency scale is frequently employed to describe audio signals (Stevens, Volkmann, and Newman 1937).”
A Real-Time Speech Enhancement Processor for Hearing Aids in 28-nm CMOS
Park, S., Lee, S., Park, J., Choi, H.-S., Lee, K., & Jeon, D. (2024) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2024.3460426 · Cited by 6
“Our SE system utilizes a Mel-scale spectrogram generated using a Mel filter [18], contrasting with the baseline that directly employs the spectrogram as neural network input and in the masking stage.”
Mohebbi-Kalkhoran, H., Makris, N. C., & Ratilal, P. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3469112 · Cited by 5
“1) Mel Spectrogram: The mel-scale is a result of psychoa-coustics [46], which is a scale of pitches judged by listeners to be equal in distance one from another and looks like a quasi-logarithmic function of acoustic frequency.”
BackgroundDiffSSD: A Diffusion-Based Dataset For Speech Forensics
Bhagtani, K., Yadav, A. K. S., Bestagini, P., & Delp, E. J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889450 · Cited by 7
“If the frequency is in mel (logarithmic) scale, it is called a mel-spectrogram [36].”
Olcay, A., White, P. R., Bull, J. M., Risch, D., Dell, B. L., & White, E. L. (2025) · The Journal of the Acoustical Society of America · doi:10.1121/10.0036498 · Cited by 5
Category variability effect in category learning with auditory stimuli
Yang, L.-X., & Wu, Y.-H. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.01122 · Cited by 5
“In order to make the scale of stimuli equal in distance from one another, we transferred the frequency f to the psychological scale mel, mel = 1127loge( f700 + 1) (Steinberg, 1937; Stevens et al., 1937).”
BackgroundResultVocal resonance as a passive biometric
Cornelius, C. T., Marois, Z., Sorber, J. M., Peterson, R., Mare, S., & Kotz, D. F. (2014) · Cited by 5
¡Qué maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline
Alnajjar, K., & Hämäläinen, M. K. (2021) · doi:10.18653/v1/2021.maiworkshop-1.9 · Cited by 11
“These features include short-time Fourier transform (Nawab and Quatieri, 1987), mel-frequency cepstral coefficients (Stevens et al., 1937), chroma, Tonnetz (Harte et al.”
BackgroundASSD: Synthetic Speech Detection in the AAC Compressed Domain
Yadav, A. K. S., Xiang, Z., Bartusiak, E. R., Bestagini, P., Tubaro, S., & Delp, E. J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095043 · Cited by 5
“A spectrogram is a 2D representation of an audio signal where the horizontal axis represents time and the vertical axis represents frequency bands [26], [27].”
BackgroundEvaluating Robustness to Noise and Compression of Deep Neural Networks for Keyword Spotting
Pereira, P. H., Beccaro, W., & Ramírez, M. A. (2023) · IEEE Access · doi:10.1109/access.2023.3280477 · Cited by 8
“Additionally, Mel spectrogram and Mel-Frequency Cep-stral Coefficients (MFCC) are time-frequency representations based on the STFT that include aspects of human auditory perception [29].”
BackgroundActive Learning Monitoring in Classroom Using Deep Learning Frameworks
Mou, A. R., Milanova, M. G., & Baillie, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37660-3_27 · Cited by 5
Wireless Eavesdropping on Wired Audio With Radio-Frequency Retroreflector Attack
Wang, G., Shi, Z., Yang, Y., et al. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3505268 · Cited by 5
“The selection of Mel-spectrogram among other spectrograms lies in the alignment with human ear’s perception, with a non-linear Mel frequency scale, expressed as Mel ( f ) = 2595 log 10 (1 + f 700 ) [27].”
Speaker Authentication for an Assistive Domotic System
Sandulescu, V. C., & Halcu, I. (2015) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs.2015.105 · Cited by 5
“F0 represents the inverse of the smallest true period in the analyzed interval [7], [8].”
Backgroundde-la-Calle-Silos, F., Valverde-Albacete, F. J., Gallardo-Antolín, A., et al. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2464691 · Cited by 5
“2) The MEL scale: The Mel scale [38] is a very well-known logarithmic transformation of the frequency scale:”
BackgroundCombrinck, H. P., & Botha, E. C. (1996) · Cited by 5
Speaker Verification and Identification
Jin, M., & Yoo, C. D. (2010) · IGI Global eBooks · doi:10.4018/978-1-60566-725-6.ch013 · Cited by 5
Zhao, H., Mao, Z., Chen, K., & Jiang, Z. (2019) · International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · doi:10.1109/sdpc.2019.00071 · Cited by 6
“In 1937, Volkmann and Newman [25] obtained the conclusion that there existed a non-linear relationship above 1000 Hz between Mel frequency and Hertz frequency through experiment.”
BackgroundEvolutionary Approximation of Instrumental Texture in Polyphonic Audio Recordings
Vatolkin, I. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185506 · Cited by 5
“…librosa implementation [17]) are based on the cepstrum (a product of the inverse Fourier transform applied to the logarithm of the squared spectrum [22]) which is further adjusted to the Mel scale, so that the distances between pitches should be perceived as similar by human listeners [23].”
BackgroundBonnet, C., Poulin-Charronnat, B., Bard, P., & Michel, C. (2020) · Acta Psychologica · doi:10.1016/j.actpsy.2020.103219 · Cited by 5
“While the frequency of audible sounds can reach 20,000 Hz, on the perceptual scale they can only reach about 2400 Mels (Zwicker & Feldtkeller, 1981).”
BackgroundInfluentialClassification of audio scenes using Narrow-Band Autocorrelation features
Valero, X., & Álías, F. (2012) · Cited by 5
Automatic Classification of Spoken Languages using Diverse Acoustic Features.
HaCohen‐Kerner, Y., & Hagege, R. (2015) · Waseda University Repository (Waseda University) · Cited by 5
Phonology versus phonetics in loanword adaptations
Peperkamp, S. (2015) · Amsterdam studies in the theory and history of linguistic science. Series 4, Current issues in linguistic theory · doi:10.1075/cilt.335.04pep · Cited by 5
Music mood classification using visual and acoustic features
Tavares, J., & Costa, Y. M. G. (2017) · Latin American Computer Conference (CLEI) · doi:10.1109/clei.2017.8226371 · Cited by 5
“Foi inicialmente proposta por [4] e, de acordo com o conceito da escala, o mesmo”
BackgroundConsiderations for Developing Sound in Golf Putting Experiments
O’Brien, B., Juhas, B. D., Bieńkiewicz, M. M. N., Pruvost, L., Buloup, F., Bringnoux, L., & Bourdin, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01692-0_23 · Cited by 5
“[41] This second mapping is known as psychometric, which was selected because it has been proven to be more indicative of how humans perceive pitch.[55] The results of the survey (Figure 1) showed participants were unable to distinguish sounds based on the τ -variable.”
ResultDuration, song section, entropy: Suggestions for a model of rapid music recognition processes
Thiesen, F. C., Kopiez, R., Müllensiefen, D., Reuter, C. W., & Czedik-Eysenberg, I. (2020) · Journal of New Music Research · doi:10.1080/09298215.2020.1784955 · Cited by 4
Comparative analysis of two different system's framework for text dependent speaker verification
Choudhury, S. P., Das, T. K., Saha, P., et al. (2015) · International Conference on Circuits, Power and Computing Technologies (ICCPCT) · doi:10.1109/iccpct.2015.7159435 · Cited by 5
Scale Position and Performed Melodic Octaves
Corso, J. F. (1954) · The Journal of Psychology · doi:10.1080/00223980.1954.9916154 · Cited by 5
Vocal fold pathology assessment using PCA and LDA
Saldanha, J. C., Thalengala, A., & Pinto, R. (2013) · doi:10.1109/issp.2013.6526890 · Cited by 5
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A survey on multi-modal and weakly supervised approaches for robust anomaly detection in video data
Barbosa, R. Z., Oliveira, H. S., & Tavares, J. M. R. S. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103388 · Cited by 10
A Survey on Deep Learning Methods for Addressing COVID-19 Issues
Raval, A. S., & Maheriya, A. P. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9304-6_7 · Cited by 4
New methods, old questions: advancing the study of unconscious perception
Jimenez, M., Prieto, A., Montoro, P. R., Hinojosa, J. A., & Kiefer, M. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1626223 · Cited by 4
Hören – Psychoakustik – Audiologie
Ellermeier, W., & Hellbrück, J. (2009) · Springer eBooks · doi:10.1007/978-3-540-34301-1_2 · Cited by 4
Signaling Patient Oxygen Desaturation with Enhanced Pulse Oximetry Tones
Sanderson, P. M., Loeb, R. G., Liley, H. G., et al. (2022) · Biomedical Instrumentation & Technology · doi:10.2345/0899-8205-56.2.46 · Cited by 4
A Novel Three Stage Framework for Person Identification From Audio Aesthetic
Iffath, F., & Gavrilova, M. L. (2022) · IEEE Access · doi:10.1109/access.2022.3200166 · Cited by 4
“The generated audio waveforms were further converted into mel-spectrograms, a logarithmic transformation of an audio signal’s frequency by utilizing mel-scales [25].”
MethodShaikh, A. A. S., Bhargavi, M. S., & Kumar, C. P. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107821 · Cited by 4
Technical communication on engine fault detection with small datasets
Scharr, C., Heinze, C., & Geist, M. (2023) · International Conference on Frontiers of Signal Processing (ICFSP) · doi:10.1109/icfsp59764.2023.10372919 · Cited by 4
“This representation is based on the perceived pitch of the human ear, called the mel scale [26], and is therefore widely used in speech and music recognition [4].”
Efficient Real-Time Smart Keyword Spotting Using Spectrogram-Based Hybrid CNN-LSTM for Edge System
Syafalni, I., Amadeus, C., Sutisna, N., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3380350 · Cited by 4
Bhagtani, K., Yadav, A. K. S., Xiang, Z., Bestagini, P., & Delp, E. J. (2023) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf59524.2023.10476982 · Cited by 5
“D mel-spectrogram S [23] using the Short Time Fourier Transform [24] with Hanning window of duration 50ms and shift of 25ms.”
BackgroundMethodFusing Speech and Language Models for Dementia Detection
Deußer, T., Siddiqi, A. M., Sparrenberg, L., Adams, T., Bauckhage, C., & Sifa, R. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825055 · Cited by 4
“Next, we apply a set of M triangular filters spaced on the Mel scale [45] to transform the linear frequency f axis to the Mel scale, which is defined as: Each Mel filter sums the energy in its corresponding frequency band: where H j [ k ] is the j -th Mel filter.”
Cansız, B., Karadeli, M. I., Aydın, N., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107991 · Cited by 4
Toward a quantitative account of pitch distribution in spontaneous narrative: Method and validation
Matteson, S. E., Olness, G. S., & Caplow, N. J. (2013) · The Journal of the Acoustical Society of America · doi:10.1121/1.4796111 · Cited by 4
Speaker Independent Speech Recognition using MFCC with Cubic-Log Compression and VQ Analysis
Kaberpanthi, N., & Datar, A. (2014) · International Journal of Computer Applications · doi:10.5120/16962-7081 · Cited by 4
“34 scale is a perceptually motivated scale [13] which is linear below 1kHz, and logarithm above, with equal numbers of samples below and above 1kHz.”
BackgroundWideband parametric speech synthesis using warped linear prediction
Raitio, T., Suni, A., Vainio, M., et al. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-363 · Cited by 4
“One of the fundamental differences between the two methods is the frequency resolution: mel-cepstrum utilizes a non-linear frequency resolution according to the Mel scale [7] while LP uses linear resolution.”
MethodMorito, T., Sugiyama, O., Uemura, S., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42007-3_49 · Cited by 4
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Nadeu, M., Simonet, M., & Llompart, M. (2017) · Probus · doi:10.1515/probus-2016-0016 · Cited by 4
CycleDRUMS: automatic drum arrangement for bass lines using CycleGAN
Barnabò, G., Trappolini, G., Lastilla, L., Campagnano, C., Fan, A., Petroni, F., & Silvestri, F. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00047-7 · Cited by 4
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Rouigueb, A., Fethi, D., Nemra, A., & Djamaa, B. (2022) · International Conference on Advanced Electrical Engineering (ICAEE) · doi:10.1109/icaee53772.2022.9962139 · Cited by 4
“MFCC coding [15] is widely used in speech processing.”
MethodAccuracy comparisons of fingerprint based song recognition approaches using very high granularity
Serrano, S., & Scarpa, M. L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14787-2 · Cited by 4
“Our proposed fingerprints are based on the estimation of the short time power spectral density (STPSD) of the audio signal obtained on a Mel frequency-scale [28].”
MethodDISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals
Colligan, T. H., Irish, K., Emlen, D. J., & Wheeler, T. J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288172 · Cited by 4
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Humayun, M. A., Yassin, H., & Abas, P. E. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.650 · Cited by 4
“It is noted that the spectrogram frequencies are mapped to the human-inspired Mel scale to emphasize the lower frequency range of human speech ( Stevens, Volkmann & Newman, 1937 ).”
BackgroundAn Open-Access System for Long-Range Chainsaw Sound Detection
Stefanakis, N., Psaroulakis, K., Simou, N., et al. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909629 · Cited by 4
“This observation is also discouraging for using mel-spectrogram as an acoustic feature since the mel scale is approximately linear up to the considered frequency limit [24].”
MethodDeveloping an interactive overview for non-visual exploration of tabular numerical information
Kildal, J. (2009) · Cited by 4
Schacht, S., Koreman, J., Lauer, C., et al. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74200-5_13 · Cited by 4
“melf = 1127.01048 · ln � 1+ f 700 (10) which was first proposed in [ 10 ].”
InfluentialCoding accuracy on the psychophysical scale
Kostal, L., & Lánský, P. (2016) · Scientific Reports · doi:10.1038/srep23810 · Cited by 4
From known to unknown: moving to unvisited locations in a novel sensorimotor map
van Vugt, F. T., & Ostry, D. J. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13608 · Cited by 4
Perceptual evaluation of measures of spectral variance
Agus, N., Anderson, H., Chen, J., et al. (2018) · The Journal of the Acoustical Society of America · doi:10.1121/1.5040484 · Cited by 4
Contemporary Methods for Speech Parameterization
Ganchev, T. D. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8447-0_1 · Cited by 5
Robust Recognition of English Speech in Noisy Environments Using Frequency Warped Signal Processing
Upadhyay, N., & Gamboa-Rosales, H. (2018) · National Academy Science Letters · doi:10.1007/s40009-017-0597-7 · Cited by 4
“In this paper, Mel scale [13], Bark scale [16], ERB (equivalent rectangular bandwidth) rate scale [17], and uniform scale (U. scale) have been used in the parameterization [18, 19].”
BackgroundMethodInfluentialCommunicating with sound (panel session
Bly, S. A., Frysinger, S. P., Lunney, D., et al. (1985) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/317456.317477 · Cited by 4
The Basics of Automatic Speech Recognition
Singh, R., Raj, B., & Virtanen, T. I. (2012) · doi:10.1002/9781118392683.ch2 · Cited by 4
Muthukumar, P. K., & Black, A. W. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.8558 · Cited by 6
“Human hearing is known to be logarithmic both in amplitude[21], and frequency[22].”
BackgroundThe diatonic foundation of hearing
Pikler, A. G. (1955) · Acta Psychologica · doi:10.1016/s0001-6918(55)80102-3 · Cited by 4
The Influence of Selected Variables on the Apparent Size of Successive Pitch Intervals
Radocy, R. E. (1978) · Psychology of Music · doi:10.1177/030573567862002 · Cited by 4
“The differing listening sets of musicians and non-musicians regarding interval consonance are discussed by Plomp and Levelt (1965) and by Terhardt (1974). Essentially, musicians label or categorise while non-musicians evaluate.”
BackgroundHuman Activity Data Discovery based on Accelerometry
Machado, I. P. (2013) · New University of Lisbon's Repository (New University of Lisbon) · Cited by 4
The use of EEG signals for biometric person recognition
Yang, S. (2015) · Kent Academic Repository (University of Kent) · Cited by 4
Modified DCTNet for audio signals classification
Xian, Y., Pu, Y., Gan, Z., et al. (2016) · The Journal of the Acoustical Society of America · doi:10.1121/1.4970932 · Cited by 4
“They are proposed based on auditory and physiological evidence of how humans perceive audio signals [2], and are closely related to the short time Fourier transform or the constant-Q transform [3].”
BackgroundCheuk, K. W., Anderson, H., Agres, K. R., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12055 · Cited by 4
“The Mel frequency scale was proposed by Stevens in 1937 as an attempt to quantify pitch such that equal differences in Mel-scale pitch correspond to equal differences in perceived pitch, regardless of the frequency in Hertz [38].”
MethodOkwuGbé: End-to-End Speech Recognition for Fon and IgboPreprint
Dossou, B. F. P., & Emezue, C. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07762 · Cited by 8
“The mel-scale is a perceptual scale of pitches judged by listeners to be equal in distance from one another (Stevens et al., 1937).”
BackgroundGonzález-Pérez, M. I., Faulhaber, B., Williams, M., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1292108/v1 · Cited by 4
Enhancing Speech Emotion Recognition through a Cross-Dataset Analysis: Exploring Improved Models
Ben-Sauod, R. M., Alshwehdi, R. S., & El-Tarhouni, W. (2024) · IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA) · doi:10.1109/mi-sta61267.2024.10599702 · Cited by 3
A Methodological Framework for Stimuli Control: Insights From Numerical Cognition
Shilat, Y., Henik, A., Galili, H., Wasserman, S., Salzmann, A., & Salti, M. (2024) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459241249185 · Cited by 5
“The distinction between the terms “number,” “numerosity,” and “quantity” is not always clear (Dos Santos, 2022; Nelson & Bartley, 1961; Stevens et al., 1937).”
Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis
Lanzino, R., Fontana, F., Cinque, L., Scarcello, F., & Maki, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91907-7_4 · Cited by 8
Ortiz-Pérez, D., Benavent-Lledó, M., Garcia-Rodriguez, J., Tomás, D., & Vizcaya-Moreno, M. F. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113787 · Cited by 6
“One such transformation is the Mel Spectrogram [106], a type of spectrogram mapped to the Mel Scale.”
Semantic Indexing of Video Simulations for Enhancing Medical Care During Crises
Jiang, S., Frigui, H., & Calhoun, A. W. (2012) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2012.95 · Cited by 3
“From each window, we extract 12 dimensional MFCC [19] and PLP features [28].”
MethodInfluentialProof of Concept of a Generic Toolkit for Sonification: The Sonification Cell in Ossia Score
Poret, M., Celerier, J.-M., Desainte‐Catherine, M., et al. (2023) · Proceedings of the ... International Conference on Auditory Display · doi:10.21785/icad2023.4039 · Cited by 3
“The perceptual scales for sound parameters are not always linear, as famously illustrated by the logarithmic perception of pitch [15], so it is important to account for any and all mapping functions when defining a sonification.”
BackgroundAutomatic Age and Gender Recognition Using Ensemble Learning
Yücesoy, E. (2024) · Applied Sciences · doi:10.3390/app14166868 · Cited by 3
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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