The DOI matches its Crossref record.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cited by
Sorted by citations
Items 101–200 of 1,004
A scale of subjective brightness.
Hanes, R. M. (1949) · Journal of Experimental Psychology · doi:10.1037/h0061367 · Cited by 67
A sociotonetic analysis of Sui dialect contact
Stanford, J. N. (2008) · Language Variation and Change · doi:10.1017/s0954394508000161 · Cited by 65
Ramloll, R., Brewster, S. A., Yu, W. M., & Riedel, B. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0353-0_32 · Cited by 64
“In addition, experiments have shown that there is no linear relationship between pitch perception and the frequency of the auditory signal ( Stevens et al., 1937 ).”
BackgroundReby, D., Andre-Obrecht, R., Galinier, A., Farinas, J., & Cargnelutti, B. (2006) · The Journal of the Acoustical Society of America · doi:10.1121/1.2358006 · Cited by 64
“Stevens et al., 1937", a human logarithmic perceptual scale, is applied to the signal in the frequency domain in order to reduce the dimensionality of the feature vector, these coefficients are called Mel frequency cepstrum coefficients !”
MethodMusic similarity measurement and recommendation system using convolutional neural networks
Fathollahi, M. S., & Razzazi, F. (2021) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-021-00206-5 · Cited by 64
Quantile based histogram equalization for noise robust speech recognition
Hilger, F., & Ney, H. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-285 · Cited by 64
Tuned with a Tune: Talker Normalization via General Auditory Processes
Laing, E. J. C., Liu, R., Lotto, A. J., & Holt, L. L. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00203 · Cited by 62
“…and Low peaks and bandwidths in the F3 frequency region (equating the peak frequency difference on the Mel scale, a psychoacoustic scale that may better model the non-linear characteristics of human auditory processing along the frequency dimension than the linear Hz scale; Stevens et al., 1937).”
BackgroundMethodYost, W. A. (2009) · Attention Perception & Psychophysics · doi:10.3758/app.71.8.1701 · Cited by 63
African elephants address one another with individually specific name-like calls
Pardo, M. A., Fristrup, K., Lolchuragi, D. S., Poole, J. H., Granli, P., Moss, C., Douglas-Hamilton, I., & Wittemyer, G. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02420-w · Cited by 62
“Moreover, as the acoustic features we extracted were based on the mel frequency scale, which was inspired by human vocal tract models 30 , it is possible that they provided peripheral measures of the principal modes of label encoding.”
MethodFechnerian metrics in unidimensional and multidimensional stimulus spaces
Dzhafarov, E. N., & Colonius, H. (1999) · Psychonomic Bulletin & Review · doi:10.3758/bf03212329 · Cited by 62
Music ControlNet: Multiple Time-Varying Controls for Music Generation
Wu, S.-L., Donahue, C., Watanabe, S., & Bryan, N. J. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3399026 · Cited by 103
“The UNet inputs Mel-scaled [35] spectrograms clipped to a dynamic range of 160 dB and scaled to [ − 1 , 1] computed from 22.05 kHz audio with a hop size of 256 (i.e., frame rate f k ≈ 86 Hz), a window size of 2048, and 160 Mel bins.”
MethodInfluentialWanrooij, K., Escudero, P., & Raijmakers, M. E. J. (2013) · Journal of Phonetics · doi:10.1016/j.wocn.2013.03.005 · Cited by 61
“…scales for the five acoustic cues to account for the fact that the human ear is better at discriminating small differences in shorter durations and lower frequencies than in longer durations and higher frequencies (e.g., Allan and Gibbon, 1991; Kewley-Port and Watson, 1994; Stevens et al., 1937).”
BackgroundMethodApplying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., Keogh, E., & Ellis, D. P. W. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.24 · Cited by 64
“These are derived from a frequency spectrum calculated on the mel scale, an early approximation to human frequency sensitivity [13].”
Speech rates converge in scripted turn-taking conversations
Schultz, B. G., O'Brien, I., Phillips, N. A., McFarland, D. H., Titone, D. A., & Palmėr, C. (2015) · Applied Psycholinguistics · doi:10.1017/s0142716415000545 · Cited by 60
“First, the acoustic signal is transformed into Mel frequencies (Stevens et al., 1937; see Figure 1b) that correspond to the subjective experience of loudness at perceptually salient frequencies for adult humans ( 20–16,000 Hz) using a crude perceptual model (for full details, see Ellis, 2007a).”
Octave equivalence as measured by similarity ratings
Kallman, H. J. (1982) · Perception & Psychophysics · doi:10.3758/bf03204867 · Cited by 61
Acoustic Features for Environmental Sound Analysis
Serizel, R., Bisot, V., Essid, S., et al. (2017) · Springer eBooks · doi:10.1007/978-3-319-63450-0_4 · Cited by 60
“Mel-scale corresponds to an approximation of the psychological sensation of heights of a pure sound (e.g., a pure sinusoid) [86].”
Increased language co-activation leads to enhanced cross-linguistic phonetic convergence
Simonet, M., & Amengual, M. (2019) · International Journal of Bilingualism · doi:10.1177/1367006919826388 · Cited by 59
AccEar: Accelerometer Acoustic Eavesdropping with Unconstrained Vocabulary
Hu, P., Zhuang, H., Santhalingam, P. S., Spolaor, R., Pathak, P. H., Zhang, G., & Cheng, X. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833716 · Cited by 65
“The Mel scale [25] is the nonlinear transformation of frequency which distorts the original audio frequency for better human perception.”
BackgroundThe Role of Sound Source Perception in Gestural Sound Description
Caramiaux, B., Bevilacqua, F. P., Bianco, T., Schnell, N., Houix, O., & Susini, P. (2014) · ACM Transactions on Applied Perception · doi:10.1145/2536811 · Cited by 58
“The original sound was analyzed in Mel bands [Stevens et al. 1937], and white noise was convoluted by the analyzed bands.”
MethodInside the spectrogram: Convolutional Neural Networks in audio processing
Dörfler, M., Bammer, R., & Grill, T. (2017) · International Conference on Sampling Theory and Applications · doi:10.1109/sampta.2017.8024472 · Cited by 57
“The mel spectrogram is derived from S0 by taking weighted averages over frequency channels defined by the mel-scale [5]:”
MethodHearing Is Believing: Biologically Inspired Methods for Robust Automatic Speech Recognition
Stern, R. M., & Morgan, N. H. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2207989 · Cited by 56
“The Mel scale (referring to the word " melody " ) is based on pitch comparisons [54].”
Tone chroma is functional in melody recognition
Kallman, H. J., & Massaro, D. W. (1979) · Perception & Psychophysics · doi:10.3758/bf03199859 · Cited by 56
Smart Audio Sensors in the Internet of Things Edge for Anomaly Detection
Antonini, M., Vecchio, M., Antonelli, F., Ducange, P., & Perera, C. (2018) · IEEE Access · doi:10.1109/access.2018.2877523 · Cited by 57
“of Nmel filters, that implements the mel-scale, the non-linear perception scale of the human ear [56].”
MethodDas, A., Guha, S., Singh, P. K., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3028241 · Cited by 54
“The approximation of Mel-frequency [30] from physical frequency can be expressed as Eqn.”
BackgroundMethodAuditory perception and cognition
Munkong, R., & Juang, B.-H. F. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.918418 · Cited by 60
“The result is a subjective pitch scale defined in " mel " [7].”
BackgroundHypoarticulation in infant-directed speech
Englund, K. T. (2017) · Applied Psycholinguistics · doi:10.1017/s0142716417000480 · Cited by 52
Auditory Cortex Processes Variation in Our Own Speech
Sitek, K. R., Mathalon, D. H., Roach, B. J., Houde, J. F., Niziolek, C. A., & Ford, J. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082925 · Cited by 59
“We used the equation m = k*log(1+(h/700)), where k is a constant (here, 2595), h is the frequency in Hertz, and m is the corresponding mel frequency [49,50].”
MethodDataset of Speech Production in intracranial Electroencephalography
Verwoert, M., Ottenhoff, M. C., Goulis, S., Colon, A., Wagner, L., Tousseyn, S., van Dijk, J. P., Kubben, P., & Herff, C. (2022) · Scientific Data · doi:10.1038/s41597-022-01542-9 · Cited by 56
The subjective size of melodic intervals over a two-octave range
Russo, F., & Thompson, W. F. (2005) · Psychonomic Bulletin & Review · doi:10.3758/bf03206445 · Cited by 52
“That is, perceptually equivalent pitch interval sizes (in mels) span progressively smaller frequency ratios with higher and higher transpositions (Stevens et al., 1937; see also Beck & Shaw, 1961).”
BackgroundMethodA CNN Approach for Audio Classification in Construction Sites
Maccagno, A., Mastropietro, A., Mazziotta, U., Scarpiniti, M., Lee, Y.-C., & Uncini, A. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-5093-5_33 · Cited by 58
“Using the Python library librosa , we extract the waveform of the audio tracks from the audio samples and, using the same library, we generate the log-scaled mel spectrogram [16] of the signal and its time derivative that will be the input to the network.”
MethodPerceptual Assessment of Voice Quality: Past, Present, and Future
Kreiman, J. E., & Gerratt, B. R. (2010) · Perspectives on Voice and Voice Disorders · doi:10.1044/vvd20.2.62 · Cited by 51
The reflexivity of cognitive science: the scientist as model of human nature
Cohen‐Cole, J. (2005) · History of the Human Sciences · doi:10.1177/0952695105058473 · Cited by 51
Shen, G., & Froud, K. (2018) · Bilingualism Language and Cognition · doi:10.1017/s136672891800038x · Cited by 48
“For example, the MEL SCALE was developed by Stevens, Volkmann, and Newman (1937) to capture stepwise sensory pitch differences, and the resultant scale does not linearly correlate with the frequency scale.”
BackgroundMethodA frequency hopping pattern inspired bionic underwater acoustic communication
Qiao, G., Ma, T., Liu, S., & Bilal, M. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101288 · Cited by 49
Multi-Modal Song Mood Detection with Deep Learning
Pyrovolakis, K., Tzouveli, P. K., & Stamou, G. B. (2022) · Sensors · doi:10.3390/s22031065 · Cited by 60
“The rendering of the name is due to Stevens, Volkmann, Newman [24] and comes from the word melody (melody).”
BackgroundWavelet Multiresolution Analysis Based Speech Emotion Recognition System Using 1D CNN LSTM Networks
Dutt, A., & Gader, P. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3277291 · Cited by 49
“…distinguishable and salient features in a voice segment to get a better recognition rate in SER. Traditionally, researchers use features like mel-frequency cepstrum coefficients (MFCC) [1], spectrograms, mel-spectrograms, energy, fundamental frequency (F0), spectral centroid, and zero-crossing rate.”
An Overview on Perceptually Motivated Audio Indexing and Classification
Richard, G., Sundaram, S., & Narayanan, S. S. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2251591 · Cited by 49
“Another scale that is based on the relationship of pitch sensation and frequency is the mel scale [3], [4].”
BackgroundFunch, O. I., Marhaug, R., Kohtala, S., & Steinert, M. (2020) · Waste Management · doi:10.1016/j.wasman.2020.09.032 · Cited by 50
Brusa, E. G. M., Delprete, C., & Maggio, L. G. D. (2021) · Applied Sciences · doi:10.3390/app112411663 · Cited by 53
“The Mel (root of the word Melody) scale [60] and the Mel Spectrogram [61] take into account the logarithmic nature of human hearing.”
BackgroundUnification of psychophysical phenomena: The complete form of Fechner’s law
Norwich, K. H., & Wong, W. (1997) · Perception & Psychophysics · doi:10.3758/bf03205509 · Cited by 49
Psychometric functions for pure-tone frequency discrimination
Dai, H., & Micheyl, C. (2011) · The Journal of the Acoustical Society of America · doi:10.1121/1.3598448 · Cited by 48
“For instance, pairs of tones separated by a constant frequency ratio (e.g., one octave) are perceived as roughly equidistant in pitch— although listeners appear to “stretch” octaves at higher frequencies (Stevens et al., 1937; Stevens and Volkmann, 1940; Ward, 1954).”
BackgroundClassification of Data Streams Applied to Insect Recognition: Initial Results
Souza, V. M. A., Silva, D. F., & Batista, G. E. A. P. A. (2013) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2013.21 · Cited by 48
“In short, MFCCs are calculated by taking the magnitudes of frequency components using an acoustically-defined scale called mel, which originated from the study of Stevens et al. [17].”
MethodMaesa, A., Garzia, F., Scarpiniti, M., et al. (2012) · Journal of Information Security · doi:10.4236/jis.2012.34041 · Cited by 47
“Psychoacoustic studies [30-32] have shown that the mind perception of the frequency content of the sound follows a nearly logarithmic scale, the Mel scale, which is linear up to 1 kHz and logarithmic thereafter:”
BackgroundRvachew, S., Mattock, K., Polka, L., et al. (2006) · The Journal of the Acoustical Society of America · doi:10.1121/1.2266460 · Cited by 47
“All first and second formant frequencies in hertz were converted to the mel scale Stevens et al. 1937 using the formula Fmels = 1127.010 481 ln 1 + Fhertz700 . c. Statistical analyses.”
MethodInfluentialMusic-induced emotions can be predicted from a combination of brain activity and acoustic features
Daly, I. P., Williams, D. A. H., Hallowell, J., Hwang, F., Kirke, A., Malik, A., Weaver, J., Miranda, E. R., & Nasuto, S. J. (2015) · Brain and Cognition · doi:10.1016/j.bandc.2015.08.003 · Cited by 47
“providing a representation of the shape of the spectrum (Stevens, 1937).”
InfluentialLimpet II: A Modular, Untethered Soft Robot
Sayed, M. E., Roberts, J. O., McKenzie, R. M., Aracri, S., Buchoux, A., & Stokes, A. A. (2020) · Soft Robotics · doi:10.1089/soro.2019.0161 · Cited by 52
“The amplitude of the powers in the FFT is then scaled(114) based on a Mel-Scale, which is a model of the sensitivity of the human ear to each frequency.(115) The resulting FFTs are used to create a heat map of frequency versus time called a Mel-Spectrogram.”
MethodSainburg, T., & Gentner, T. Q. (2021) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2021.811737 · Cited by 48
“Mel-scaling ( Figure 2G ), is a form of log-scaling fit to fit human perception (Stevens et al., 1937), though the specific scaling range relative to human perception are imperfect (Greenwood, 1997).”
BackgroundON THE PROBLEM OF SCALES FOR THE MEASUREMENT OF PSYCHOLOGICAL MAGNITUDES
Stevens, S. S. (2006) · Proceedings of Fechner Day · Cited by 47
Self-citationThe Perception of Tonal Structure Through the Differentiation and Organization of Pitches.
Smith, N. A., & Schmuckler, M. A. (2004) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/0096-1523.30.2.268 · Cited by 45
“West and Fryer (1990), for example, found that after hearing random orderings of the diatonic set, listeners were unable to identify the tonic of the sequence, indicative of a failure of tonal perception.”
BackgroundA Direct Method for Measuring Discounting and QALYs More Easily and Reliably
Attema, A. E., Bleichrodt, H., & Wakker, P. P. (2012) · Medical Decision Making · doi:10.1177/0272989x12451654 · Cited by 44
van Noorden, L. (1982) · Springer eBooks · doi:10.1007/978-1-4684-8917-0_13 · Cited by 46
Optimization of data-driven filterbank for automatic speaker verification
Sarangi, S. K., Sahidullah, M., & Saha, G. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102795 · Cited by 50
“It uses the mel scale [48] based triangular filterbank for the creation of cepstral features.”
MethodThe Perception of Musical Tones
Rasch, R. A., & Plomp, R. (1982) · Psychology of Music · doi:10.1016/b978-0-12-213562-0.50005-x · Cited by 45
Automatic Wheezing Detection Using Speech Recognition Technique
Lin, B.-S., & Lin, B. (2016) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-016-0161-9 · Cited by 44
“The mel scale frequencies closely approximate the response of the human auditory system [22].”
BackgroundRobust Sensorimotor Learning during Variable Sentence-Level Speech
Lametti, D. R., Smith, H. J., Watkins, K. E., & Shiller, D. M. (2018) · Current Biology · doi:10.1016/j.cub.2018.07.030 · Cited by 41
Deep Recurrent Neural Networks for Audio Classification in Construction Sites
Scarpiniti, M., Comminiello, D., Uncini, A., et al. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287802 · Cited by 44
“Mel spectrogram – It represents the spectrogram in the Mel scale [17].”
BackgroundDynamic frequency change influences loudness perception: A central, analytic process.
Neuhoff, J. G., McBeath, M. K., & Wanzie, W. C. (1999) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.25.4.1050 · Cited by 44
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. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ace8be · Cited by 42
“For reconstruction, we chose mel-spectrograms of speech as they represent sound acoustics on a spectrum inspired by human auditory perception [61].”
Auracher, J., Scharinger, M., & Menninghaus, W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216930 · Cited by 44
de Benito-Gorrón, D., Lozano-Díez, A., Toledano, D. T., & González-Rodríguez, J. (2019) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-019-0152-1 · Cited by 48
“This representation is a timefrequency matrix where the frequency axis follows the mel-frequency scale [30], a log-based perceptual representation of the spectrum.”
BackgroundDeep Sensing of Breathing Signal During Conversational Speech
Nallanthighal, V. S., Härmä, A., & Strik, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1796 · Cited by 45
“Mel filter banks use Mel-frequency scaling, which is a perceptual scale to replicate human ear perception of sound [12].”
BackgroundIntrospectionist and behaviorist interpretations of ratio scales of perceptual magnitudes.
Savage, C. W. (1966) · The Psychological Monographs · doi:10.1037/h0092980 · Cited by 43
Elmasian, R., & Birnbaum, M. H. (1984) · Perception & Psychophysics · doi:10.3758/bf03207513 · Cited by 43
Filterbank learning for deep neural network based polyphonic sound event detection
Çakır, E., Ozan, E. C., & Virtanen, T. I. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727634 · Cited by 39
“On the other hand, the empirical nature of mel scale leads to several handcrafted mel formulas [12], [13], [14], [15], [16], all of which provide only an approximation to the real human perception.”
BackgroundFluency Profiling System: An automated system for analyzing the temporal properties of speech
Little, D. R., Oehmen, R., Dunn, J. C., Hird, K., & Kirsner, K. (2012) · Behavior Research Methods · doi:10.3758/s13428-012-0222-0 · Cited by 41
Modulations of the auditory M100 in an imitation task
Franken, M. K., Peter Hagoort, A., & Acheson, D. J. (2015) · Brain and Language · doi:10.1016/j.bandl.2015.01.001 · Cited by 42
The creation of a new vowel category by adult learners after adaptive phonetic training
Grenon, I., Kubota, M., & Sheppard, C. (2018) · Journal of Phonetics · doi:10.1016/j.wocn.2018.10.005 · Cited by 41
Amengual, M., & Simonet, M. (2019) · Linguistic Approaches to Bilingualism · doi:10.1075/lab.18042.ame · Cited by 41
Acoustic and linguistic factors affecting perceptual dissimilarity judgments of voices
Perrachione, T. K., Furbeck, K. T., & Thurston, E. J. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5126697 · Cited by 44
“Correspondingly, we applied arcsine transformation (Studebaker, 1985) to the dissimilarity rating data prior to inferential statistics, after which the data were not as skewed toward the extrema of the scale, but were nonetheless still not normally distributed (A ¼ 4094.5, p 0.0001).”
MethodInfluentialZhao, H., Zhang, J., Jiang, Z., Wei, D., Zhang, X., & Mao, Z. (2019) · Sensors · doi:10.3390/s19112590 · Cited by 41
“In 1937, Volkmann and Newman [36], through experimentation, reached the conclusion that there existed a non-linear relationship above 1000 Hz between the Mel frequency and Hertz frequency.”
BackgroundMaggio, L. G. D. (2022) · Sensors · doi:10.3390/s23010211 · Cited by 43
“The VGGish architecture [37] summarized in Table 1 contains 62 million weights. model was originally trained by Hershey et al. [37] in 2017 by using 70 The model was originally trained by Hershey et al. [37] in 2017 by using 70 million YouTube clips, for a total amount of 5.24 million hours and…”
BackgroundMethodInfluentialA Comparative Study Of LPCC And MFCC Features For The Recognition Of Assamese Phonemes
Bhattacharjee, U. (2013) · Cited by 40
I Sense You by Breath: Speaker Recognition via Breath Biometrics
Lu, L., Liu, L., Hussain, M. J., & Liu, Y. (2017) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2017.2767587 · Cited by 39
The Teager-Kaiser Energy Cepstral Coefficients as an Effective Structural Health Monitoring Tool
Civera, M., Ferraris, M., Ceravolo, R., et al. (2019) · Applied Sciences · doi:10.3390/app9235064 · Cited by 40
“…with a 40 The definition of its base unit, the Mel, is one-thousandth of the pitch ( ℘ ) of a simple tone with a 40 dB amplitude above the auditory threshold and a frequency that is equal to 1000 Hz [38,46]. dB amplitude above the auditory threshold and a frequency that is equal to 1000 Hz [38,46].”
BackgroundMethodHeelan, C., Lee, J.-H., O’Shea, R. T., Lynch, L., Brandman, D. M., Truccolo, W., & Nurmikko, A. V. (2019) · Communications Biology · doi:10.1038/s42003-019-0707-9 · Cited by 40
Comparing two tiny giants or two huge dwarfs? Preference reversals owing to number size framing
Wong, K. F. E., & Kwong, J. Y. Y. (2005) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2005.04.002 · Cited by 40
Encoding and decoding of meaning through structured variability in intonational speech prosody
Xie, X., Buxó‐Lugo, A., & Kurumada, C. (2021) · Cognition · doi:10.1016/j.cognition.2021.104619 · Cited by 37
Greenwood, D. D. (1997) · Hearing Research · doi:10.1016/s0378-5955(96)00175-x · Cited by 39
Xie, X., Jaeger, T. F., & Kurumada, C. (2023) · Cortex · doi:10.1016/j.cortex.2023.05.003 · Cited by 45
Video Summarization with LSTM and Deep Attention Models
Lebron, L., & Koblents, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05716-9_6 · Cited by 35
Auditory Interfaces and Sonification
Nees, M. A., & Walker, B. N. (2009) · Human factors and ergonomics · doi:10.1201/9781420064995-c32 · Cited by 37
“Like detection, researchers have studied the discriminability of sounds along a wealth of dimensions such as pitch (e.g., Stevens, Volkmann, and Newman, 1937; Turnbull, 1944), loudness (e.g., Stevens, 1936), tempo (e.g., Boltz, 1998), and duration (e.g., Jeon and Fricke, 1997), to name but a few.”
BackgroundThompson, W. F. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-381460-9.00004-3 · Cited by 40
Low Bit-Rate Speech Coding through Quantization of Mel-Frequency Cepstral Coefficients
Boucheron, L. E., Leon, P. L. D., & Sandoval, S. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2162407 · Cited by 37
“…a set of weighting functions to the power spectrum prior to the discrete cosine transform (DCT) and log operations [10] (5) This weighting is based on human perception of pitch [9] and is most commonly implemented in the form of a bank of filters each with a triangular frequency response [10] .”
Suitable CNN Weight Initialization and Activation Function for Javanese Vowels Classification
Dewa, C. K., & Afiahayati (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.512 · Cited by 39
Multi-speaker Emotional Acoustic Modeling for CNN-based Speech Synthesis
Choi, H., Park, S., Park, J., & Hahn, M. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683682 · Cited by 35
“After the mapping from linguistic features to acoustic features was learned, the generated acoustic features were fed to WaveNet vocoder [13] to synthesize speech waveform.”
BackgroundMethodScene-Aware Audio Rendering via Deep Acoustic Analysis
Tang, Z., Bryan, N. J., Li, D., et al. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.2973058 · Cited by 37
“Then we use 32 Mel-scale bands and area normalization for Mel-frequency warping [62].”
MethodNallanthighal, V. S., Mostaani, Z., Härmä, A., Strik, H., & Magimai.-Doss, M. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.029 · Cited by 35
Sharan, R. V., Xiong, H., & Berkovsky, S. (2021) · Sensors · doi:10.3390/s21103434 · Cited by 45
“The mel-filter, as used in computing mel-frequency cepstral coefficients (MFCCs) [37], has frequency bands equally spaced on the mel-scale [38] resembling the way humans perceive sound.”
MethodDomingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · IEEE Access · doi:10.1109/access.2022.3220265 · Cited by 37
“Mel’s scale is empirically based on the way humans perceive sound [47].”
BackgroundCai, T., Ni, H., Yu, M., Huang, X., Wong, K., Volpi, J. J., Wang, J. Z., & Wong, S. T. C. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102522 · Cited by 49
“In this work, we use the Mel Scale (Stevens et al., 1937) and Fast Fourier Transform (FFT) to transform the audio signal before plotting the spectrogram.”
Limitations of difference-in-difference for measuring convergence
Priva, U. C., & Sanker, C. (2019) · Laboratory Phonology Journal of the Association for Laboratory Phonology · doi:10.5334/labphon.200 · Cited by 34
Thomas, M., Jensen, F. H., Averly, B., Demartsev, V., Manser, M. B., Sainburg, T., Roch, M. A., & Strandburg‐Peshkin, A. (2022) · Journal of Animal Ecology · doi:10.1111/1365-2656.13754 · Cited by 35
Morito, T., Sugiyama, O., Kojima, R., & Nakadai, K. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759215 · Cited by 40
“1) Acoustic Features: We adopted Mel filter bank features [16] as the acoustic features used as the input of the networks.”
MethodWhitman, B., & Vercoe, B. L. (2005) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 35
ESResNe(X)t-fbsp: Learning Robust Time-Frequency Transformation of Audio
Guzhov, A., Raue, F., Hees, J., & Dengel, A. R. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533654 · Cited by 42
“The architecture of the Piczak-CNN followed its custom design and was combined with Melscaled power spectrograms [13] .”
MethodBarrios, S. L., Jiang, N., & Idsardi, W. J. (2016) · Second language Research · doi:10.1177/0267658316630784 · Cited by 34
“Moreover, alternative scales, such as Mels and Barks, have been argued to be psychoacoustically more realistic units for such comparisons (Stevens et al., 1937; Zwicker, 1961).”
BackgroundLewis, D. (1948) · The Journal of Psychology · doi:10.1080/00223980.1948.9917417 · Cited by 34
Contrastive Learning of Musical Representations
Spijkervet, J., & Burgoyne, J. (2021) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.2103.09410 · Cited by 132
Distributed learning of fully connected neural networks using independent subnet training
Yuan, B., Wolfe, C. R., Dun, C., Tang, Y., Kyrillidis, A., & Jermaine, C. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3529337.3529343 · Cited by 55
“We represent each waveform as a 4096-dimensional feature vector [67].”
MethodVariability of articulator positions and formants across nine English vowels
Whalen, D. H., Chen, W., Tiede, M., & Nam, H. (2018) · Journal of Phonetics · doi:10.1016/j.wocn.2018.01.003 · Cited by 32
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
