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 prototype (or nonprototype) vowel was equated in psychophysical terms using the mel scale.
“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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Stevens, S. S. (1957) · Psychological Review · doi:10.1037/h0046162 · Cited by 3,388 · see citing works
Self-citationlibrosa: Audio and Music Signal Analysis in Python
McFee, B., Raffel, C., Liang, D., Ellis, D. P. W., McVicar, M., Battenberg, E., & Nieto, O. (2015) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-7b98e3ed-003 · Cited by 3,099 · see citing works
Kuhl, P. K. (1991) · Perception & Psychophysics · doi:10.3758/bf03212211 · Cited by 1,331 · see citing works
Cross-language analysis of phonetic units in language addressed to infants
Kuhl, P. K., Andruski, J. E., Chistovich, I. A., Chistovich, L. A., Kozhevnikova, E. V., Ryskina, V. L., Stolyarova, E. I., Sundberg, U., & Lacerda, F. (1997) · Science · doi:10.1126/science.277.5326.684 · Cited by 1,041 · see citing works
Ratio scales and category scales for a dozen perceptual continua.
Stevens, S. S., & Galanter, E. (1957) · Journal of Experimental Psychology · doi:10.1037/h0043680 · Cited by 1,059 · see citing works
Self-citationFernald, A. E., Taeschner, T., Dunn, J., Papoušek, M., de Boysson-Bardies, B., & Fukui, I. (1989) · Journal of Child Language · doi:10.1017/s0305000900010679 · Cited by 901 · see citing works
Krumhansl, C. L., & Kessler, E. J. (1982) · Psychological Review · doi:10.1037/0033-295x.89.4.334 · Cited by 905 · see citing works
BirdNET: A deep learning solution for avian diversity monitoring
Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101236 · Cited by 903 · see citing works
“We performed frequency compression using a mel scale with 64 bands and a break frequency at 1750 Hz — considerably above the original proposal (Stevens et al., 1937) to achieve approximate linear scaling up to 500 Hz.”
MethodA perceptual interference account of acquisition difficulties for non-native phonemes
Iverson, P., Kuhl, P. K., Akahane-Yamada, R., Diesch, E., Tohkura, Y., Kettermann, A., & Siebert, C. (2002) · Cognition · doi:10.1016/s0010-0277(02)00198-1 · Cited by 653 · see citing works
“The formant frequencies were spaced equally on the mel scale (Stevens et al., 1937).”
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Issa, D., Demirci, M. F., & Yazıcı, A. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101894 · Cited by 621 · see citing works
AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app
Imran, A., Posokhova, I., Qureshi, H. N., Masood, U., Riaz, M. S., Ali, K., John, C. N., Hussain, M. I., & Nabeel, M. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100378 · Cited by 621 · see citing works
“We used the Mel spectrogram over a typical frequency spectrogram because the Mel scale in the Mel spectrogram has unequal spacing in the frequency bands and provides a higher resolution (more informative) in lower frequencies and vice versa, as compared to equally spaced frequency bands in normal spectrogram [63].”
MethodBRIDGING THE GAP BETWEEN L2 SPEECH PERCEPTION RESEARCH AND PHONOLOGICAL THEORY
Escudero, P., & Boersma, P. (2004) · Studies in Second Language Acquisition · doi:10.1017/s0272263104040021 · Cited by 459 · see citing works
Quantification of the hierarchy of tonal functions within a diatonic context.
Krumhansl, C. L., & Shepard, R. N. (1979) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.5.4.579 · Cited by 418 · see citing works
The psychological representation of musical pitch in a tonal context
Krumhansl, C. L. (1979) · Cognitive Psychology · doi:10.1016/0010-0285(79)90016-1 · Cited by 410 · see citing works
“The work of Stevens (Stevens & Volkmann, 1940; Stevens et al., 1937) indicates that tones close in terms of pitch height should be judged more closely related than tones farther apart in pitch height.”
BackgroundGeometrical approximations to the structure of musical pitch.
Shepard, R. N. (1982) · Psychological Review · doi:10.1037/0033-295x.89.4.305 · Cited by 385 · see citing works
Statistical phonetic learning in infants: facilitation and feature generalization
Maye, J., Weiss, D. J., & Aslin, R. N. (2008) · Developmental Science · doi:10.1111/j.1467-7687.2007.00653.x · Cited by 376 · see citing works
Iverson, P., & Kuhl, P. K. (1995) · The Journal of the Acoustical Society of America · doi:10.1121/1.412280 · Cited by 352 · see citing works
Pitch as a Medium: A New Approach to Psychophysical Scaling
Attneave, F., & Olson, R. K. (1971) · The American Journal of Psychology · doi:10.2307/1421351 · Cited by 345 · see citing works
An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
Michelsanti, D., Tan, Z., Zhang, S.-X., Xu, Y., Yu, M., Yu, D., & Jensen, J. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3066303 · Cited by 326 · see citing works
“• The frequency resolution of the human auditory system is not uniform, showing a higher discrimination for low frequencies [235].”
BackgroundAcoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
Briggs, F., Lakshminarayanan, B., Neal, L., Fern, X. Z., Raich, R., Frey, S. J. K., Hadley, A. S., & Betts, M. G. (2012) · The Journal of the Acoustical Society of America · doi:10.1121/1.4707424 · Cited by 305 · see citing works
Look, Listen, and Learn More: Design Choices for Deep Audio Embeddings
Cramer, A. L., Wu, H.-H., Salamon, J., & Bello, J. P. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682475 · Cited by 289 · see citing works
“Mel spectrograms are designed to capture perceptually relevant information more efficiently with less frequency bands compared to a linear spectrogram [29].”
BackgroundFeldman, N. H., Griffiths, T. L., & Morgan, J. L. (2009) · Psychological Review · doi:10.1037/a0017196 · Cited by 266 · see citing works
“The stimuli were designed to be equally spaced when measured along the mel scale, which equates distances on the basis of difference limens (S. S. Stevens et al., 1937).”
Does Audio Deepfake Detection Generalize?
Müller, N., Czempin, P., Diekmann, F., Froghyar, A., & Böttinger, K. (2022) · Interspeech 2022 · doi:10.21437/interspeech.2022-108 · Cited by 258 · see citing works
Middya, A. I., Nag, B., & Roy, S. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108580 · Cited by 250 · see citing works
Speech synthesis from ECoG using densely connected 3D convolutional neural networks
Angrick, M., Herff, C., Mugler, E., Tate, M. C., Slutzky, M. W., Krusienski, D. J., & Schultz, T. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0c59 · Cited by 225 · see citing works
“The compression is based on the usage of the mel-scale which condenses frequency bands in accordance to the human perception [53].”
BackgroundMethodTowards End-to-End Prosody Transfer for Expressive Speech Synthesis with TacotronPreprint
Skerry-Ryan, R., Battenberg, E., Xiao, Y., Wang, Y., Stanton, D., Shor, J., Weiss, R. J., Clark, R., & Saurous, R. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09047 · Cited by 348 · see citing works
“In this work, we decided to use the same perceptually-relevant summarization of the spectrum that (Wang et al., 2017a) does: the mel-warped spectrum (Stevens et al., 1937).”
MethodFeatures for Content-Based Audio Retrieval
Mitrović, D., Zeppelzauer, M., & Breiteneder, C. J. (2010) · Advances in computers · doi:10.1016/s0065-2458(10)78003-7 · Cited by 208 · see citing works
“Filters in the domain of audio feature extraction are often based on Bark- [203], ERB- [126], and Mel-scale [171].”
MethodInfluentialAn Axiomatization of Utility Based on the Notion of Utility Differences
Suppes, P., & Winet, M. (1955) · Management Science · doi:10.1287/mnsc.1.3-4.259 · Cited by 208 · see citing works
Marks, L. E. (1974) · Perception & Psychophysics · doi:10.3758/bf03203956 · Cited by 197 · see citing works
Vowel Category Boundaries Enhance Cortical and Behavioral Responses to Speech Feedback Alterations
Niziolek, C. A., & Guenther, F. H. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.1008-13.2013 · Cited by 189 · see citing works
“…of hertz because the mel scale better reflects the sensitivities of the ear to changes in different regions of frequency space; that is, a fixed distance in mel space is approximately equally discriminable regardless of where along the frequency dimension it is located (Stevens et al., 1937).”
BackgroundStructural Representations of Musical Pitch
Shepard, R. N. (1982) · Psychology of Music · doi:10.1016/b978-0-12-213562-0.50015-2 · Cited by 194 · see citing works
LeBien, J. G., Zhong, M., Campos‐Cerqueira, M., Velev, J. P., Dodhia, R. M., Ferres, J. L., & Aide, T. M. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101113 · Cited by 189 · see citing works
“The mel scale refers to a perceptual scale of pitch based on an empirical study of human hearing (Stevens et al., 1937).”
Categorization of speech by infants: Support for speech-sound prototypes.
Grieser, D., & Kuhl, P. K. (1989) · Developmental Psychology · doi:10.1037/0012-1649.25.4.577 · Cited by 184 · see citing works
Effects of language experience: neural commitment to language-specific auditory patterns
Zhang, Y., Kuhl, P. K., Imada, T., Kotani, M., & Tohkura, Y. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.040 · Cited by 179 · see citing works
Modelling trial-by-trial changes in the mismatch negativity
Lieder, F., Daunizeau, J., Garrido, M. I., Friston, K. J., & Stephan, K. E. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002911 · Cited by 168 · see citing works
“We assume that the subjects’ initial tone categories approximately correspond to musical notes, because for pure tones subjects’ auditory representations are likely to be shaped by musical experience, and pitch perception becomes increasingly logarithmic for frequencies above 500 Hz [47].”
BackgroundPerceptual primacy of dimensions: Support for a model of dimensional interaction.
Melara, R. D., & Marks, L. E. (1990) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.16.2.398 · Cited by 168 · see citing works
M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation
Chudasama, V. M., Kar, P., Gudmalwar, A. P., Shah, N. J., Wasnik, P., & Onoe, N. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00511 · Cited by 170 · see citing works
“We also use 128 Mel filter banks to generate the Mel Spectrogram [30].”
MethodThe frequency scale of speech intonation
Hermes, D. J., & van Gestel, J. C. (1991) · The Journal of the Acoustical Society of America · doi:10.1121/1.402397 · Cited by 166 · see citing works
“" (Stevens et al., 1937; Stevens and Volkman, 1940).”
Melody recognition: The experimental application of musical rules.
Cuddy, L. L., Cohen, A. J., & Miller, J. (1979) · Canadian Journal of Psychology/Revue Canadienne de Psychologie · doi:10.1037/h0081713 · Cited by 156 · see citing works
Visual intensity judgments: An empirical rule and a theory.
Warren, R. M. (1969) · Psychological Review · doi:10.1037/h0026732 · Cited by 154 · see citing works
A survey on sound source localization in robotics: From binaural to array processing methods
Argentieri, S., Danès, P., & Souères, P. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.03.003 · Cited by 152 · see citing works
“This is one of the main features of the human auditory system, which confirms our better ability to discriminate low frequencies [22].”
Alafif, T., Tehame, A. M., Bajaba, S. M., Barnawi, A., & Zia, S. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18031117 · Cited by 162 · see citing works
Sound graphs: A numerical data analysis method for the blind
Mansur, D. L., Blattner, M. M., & Joy, K. I. (1985) · Journal of Medical Systems · doi:10.1007/bf00996201 · Cited by 144 · see citing works
Deep Spoken Keyword Spotting: An Overview
López‐Espejo, I., Tan, Z., Hansen, J. H. L., & Jensen, J. (2021) · IEEE Access · doi:10.1109/access.2021.3139508 · Cited by 144 · see citing works
“Speech features based on the perceptually-motivated Melscale filterbank [80], like the log-Mel spectral coefficients and Mel-frequency cepstral coefficients (MFCCs) [81], have”
MethodSegbroeck, M. V., Knoll, A. T., Levitt, P. R., & Narayanan, S. S. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.005 · Cited by 137 · see citing works
Herff, C., Diener, L., Angrick, M., Mugler, E., Tate, M. C., Goldrick, M. A., Krusienski, D. J., Slutzky, M. W., & Schultz, T. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01267 · Cited by 118 · see citing works
“To better represent the human perception of speech, we compressed the speech spectrogram to the Mel-scale (Stevens et al., 1937) using 40 logarithmically-spaced triangular filter banks.”
MethodThe Perception of Prosodic Prominence
Terken, J. M. B., & Hermes, D. J. (2000) · Text, speech and language technology · doi:10.1007/978-94-015-9413-4_5 · Cited by 132 · see citing works
Preserving privacy in speaker and speech characterisation
Nautsch, A., Jiménez, A., Treiber, A., Kolberg, J., Jasserand, C., Kindt, E. J., Delgado, H., Todisco, M., Hmani, M. A., Mtibaa, A., et al. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.06.001 · Cited by 128 · see citing works
“While there are a host of alternatives, the most popular acoustic features are mel-frequency cepstrum coefficients (MFCCs) (Stevens et al., 1937; Bridle and Brown, 1974; Davis and Mermelstein, 1980).”
BackgroundIndividual identification via electrocardiogram analysis
Fratini, A., Sansone, M., Bifulco, P., & Cesarelli, M. (2015) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0072-y · Cited by 126 · see citing works
“A triangular band pass filter bank is applied in the frequency domain to reveal the mel-frequency components [99].”
MethodGarcía-Ordás, M. T., Benítez‐Andrades, J. A., García-Rodríguez, I., et al. (2020) · Sensors · doi:10.3390/s20041214 · Cited by 127 · see citing works
Phonetic consequences of dynamic cross-linguistic interference in proficient bilinguals
Simonet, M. (2014) · Journal of Phonetics · doi:10.1016/j.wocn.2014.01.004 · Cited by 123 · see citing works
Li, J., Kang, D., Pei, W., Zhe, X., Zhang, Y., He, Z., & Bao, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01110 · Cited by 129 · see citing works
The Role of Pitch and Timbre in Voice Gender Categorization
Pernet, C. R., & Belin, P. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00023 · Cited by 125 · see citing works
“One possibility is that differences of pitch in our stimuli were too small for extreme stimuli since we used a linear morph among stimuli but auditory perception follows a log scale (Stevens et al., 1937).”
MethodPerceived harmonic structure of chords in three related musical keys.
Krumhansl, C. L., Bharucha, J. J., & Kessler, E. J. (1982) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.8.1.24 · Cited by 125 · see citing works
A Convolutional Neural Network Smartphone App for Real-Time Voice Activity Detection
Sehgal, A., & Kehtarnavaz, N. (2018) · IEEE Access · doi:10.1109/access.2018.2800728 · Cited by 122 · see citing works
“A log-mel energy spectrum represents the short-term power of an audio signal in the mel-frequency scale [21] over some time duration.”
An Active Learning Paradigm for Online Audio-Visual Emotion Recognition
Kansizoglou, I., Bampis, L., & Gasteratos, A. C. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2961089 · Cited by 125 · see citing works
Angrick, M., Ottenhoff, M. C., Diener, L., Ivucic, D., Ivucic, G., Goulis, S., Saal, J., Colon, A. J., Wagner, L., Krusienski, D. J., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02578-0 · Cited by 125 · see citing works
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Fedorov, I., Stamenovic, M., Jensen, C., Yang, L.-C., Mandell, A., Gan, Y., Mattina, M., & Whatmough, P. N. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1864 · Cited by 116 · see citing works
“In all cases, the spectral input to the network is mapped onto a 128-dimensional mel space [24] and power-law compressed with an exponent of 0.”
MethodMagnitude estimation of conceptual data dimensions for use in sonification.
Walker, B. N. (2002) · Journal of Experimental Psychology Applied · doi:10.1037//1076-898x.8.4.211 · Cited by 116 · see citing works
Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus
Wilson, G. H., Stavisky, S. D., Willett, F. R., Avansino, D. T., Kelemen, J. N., Hochberg, L. R., Henderson, J. M., Druckmann, S., & Shenoy, K. V. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abbfef · Cited by 103 · see citing works
ESResNet: Environmental Sound Classification Based on Visual Domain Models
Guzhov, A., Raue, F., Hees, J., & Dengel, A. R. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413035 · Cited by 121 · see citing works
“The first model that set the baseline for the environmental sound classification was the CNN proposed in [10] (Piczak-CNN) that operated on Mel-scaled [11] spectrograms.”
MethodBadami, K. M. H., Lauwereins, S., Meert, W., & Verhelst, M. (2015) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2015.2487276 · Cited by 107 · see citing works
“Each band allows further amplification and does a BPF operation with exponentially spaced fc to mimic human hearing [21].”
BackgroundThe logic of psychophysical measurement.
Bergmann, G., & Spence, K. W. (1944) · Psychological Review · doi:10.1037/h0055474 · Cited by 107 · see citing works
Origins and Development of Generalized Magnitude Representation
Lourenco, S. F., & Longo, M. R. (2011) · Elsevier eBooks · doi:10.1016/b978-0-12-385948-8.00015-3 · Cited by 110 · see citing works
“Pitch is another dimension lacking intrinsic polarity [84].”
BackgroundSynthesizing Audio with Generative Adversarial Networks
Donahue, C., McAuley, J., & Puckette, M. (2018) · Cited by 103 · see citing works
Søndergaard, P. L., & Majdak, P. (2013) · Springer eBooks · doi:10.1007/978-3-642-37762-4_2 · Cited by 103 · see citing works
“These include the just-noticeable difference in frequency, giving rise to the mel scale [74] and its variants [26].”
BackgroundWard, W. D. (1999) · Elsevier eBooks · doi:10.1016/b978-012213564-4/50009-3 · Cited by 103 · see citing works
Auditory Displays for In-Vehicle Technologies
Nees, M. A., & Walker, B. N. (2011) · Reviews of Human Factors and Ergonomics · doi:10.1177/1557234x11410396 · Cited by 99 · see citing works
On the cognitive basis of contact-induced sound change: Vowel merger reversal in Shanghainese
Yao, Y., & Chang, C. B. (2016) · Language · doi:10.1353/lan.2016.0031 · Cited by 100 · see citing works
Acoustic analysis of lexical tone in Mandarin infant-directed speech.
Liu, H.-M., Tsao, F., & Kuhl, P. K. (2007) · Developmental Psychology · doi:10.1037/0012-1649.43.4.912 · Cited by 99 · see citing works
“Prosodic modifications in IDS may facilitate communication development by eliciting attention and positive affect during adult–child interaction (Cooper & Aslin, 1990; Fernald & Kuhl, 1987; Papousek, Papousek, & Symmes, 1991; Werker & McLeod, 1989).”
BackgroundPlack, C. J., & Oxenham, A. J. (2006) · Springer eBooks · doi:10.1007/0-387-28958-5_2 · Cited by 97 · see citing works
“The “mel scale” was derived by Stevens et al. (1937) by requiring listeners to adjust the frequency of a comparison tone until the pitch sounded half that of a standard.”
MethodElimination of Biases in Loudness Judgments for Tones
Warren, R. M. (1970) · The Journal of the Acoustical Society of America · doi:10.1121/1.1912298 · Cited by 97 · see citing works
Angrick, M., Luo, S., Rabbani, Q., Candrea, D. N., Shah, S., Milsap, G., Anderson, W. S., Gordon, C. R., Rosenblatt, K. R., Clawson, L., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-60277-2 · Cited by 96 · see citing works
Ross, E. D., Edmondson, J. A., Seibert, G. B., & Homan, R. W. (1988) · Brain and Language · doi:10.1016/0093-934x(88)90058-2 · Cited by 94 · see citing works
Speech recognition with artificial neural networks
Dede, G., & Sazlı, M. H. (2009) · Digital Signal Processing · doi:10.1016/j.dsp.2009.10.004 · Cited by 94 · see citing works
“Thus, a filter much more similar to human ear is obtained [10].”
BackgroundExploring Low Cost Laser Sensors to Identify Flying Insect Species
Silva, D. F., Souza, V. M. A., Ellis, D. P. W., Keogh, E., & Batista, G. (2015) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-014-0168-9 · Cited by 93 · see citing works
“[34].”
Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“The Mel scale [34] is a logarithmic scale, emphasising low-frequency signals over highfrequency ones, and it is sometimes used for acoustic signal representation, aiming to mimic the human aural perception.”
BackgroundSanchez, F. J. B., Hossain, M. R., English, N. B., & Moore, S. T. (2021) · Scientific Reports · doi:10.1038/s41598-021-95076-6 · Cited by 93 · see citing works
Iverson, P., & Kuhl, P. K. (2000) · Perception & Psychophysics · doi:10.3758/bf03206929 · Cited by 92 · see citing works
Multi- and Cross-Modal Semantics Beyond Vision: Grounding in Auditory Perception
Kiela, D., & Clark, S. C. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1293 · Cited by 85 · see citing works
“Such features are derived from the mel-frequency cepstrum representation of an audio fragment (Stevens et al., 1937).”
BackgroundA unified account of categorical effects in phonetic perception
Kronrod, Y., Coppess, E., & Feldman, N. H. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1049-y · Cited by 87 · see citing works
The Perception of Musical Tones
Rasch, R. A., & Plomp, R. (1999) · Elsevier eBooks · doi:10.1016/b978-012213564-4/50005-6 · Cited by 90 · see citing works
Ross, E. D., Edmondson, J. A., & Seibert, G. B. (1986) · Journal of Phonetics · doi:10.1016/s0095-4470(19)30669-2 · Cited by 89 · see citing works
Anomalous sound event detection: A survey of machine learning based methods and applications
Mnasri, Z., Stefano Rovetta, A., & Masulli, F. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11817-9 · Cited by 86 · see citing works
“Mel-frequency cepstral coefficients (MFCC) The Mel-frequency cepstrum is a representation based on the cosine transform of a log-power spectrum, computed on a bio-logically-motivated nonlinear scale of frequency, ı.e. the Mel scale [125].”
MethodAudio Classification of Bird Species: A Statistical Manifold Approach
Briggs, F., Raich, R., & Fern, X. Z. (2009) · Proceedings · doi:10.1109/icdm.2009.65 · Cited by 86 · see citing works
“The idea is to first compute Mel-frequency coefficients (MFCs), which are like the magnitude spectrum, but in units of mels rather than Hz (mels correspond more closely with human perception of pitch [30]).”
BackgroundFeature learning with deep scattering for urban sound analysis
Salamon, J., & Bello, J. P. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362478 · Cited by 83 · see citing works
“The mel-spectrogram is obtained by taking the short-time Fourier transform and mapping its spectral magnitudes onto the perceptually motivated mel-scale [21] using a filterbank in the frequency domain.”
MethodEmotion Detection from Speech Signals using Voting Mechanism on Classified Frames
Zamil, A. A. A., Hasan, S., Baki, S. M. J., Adam, J. M., & Zaman, I. (2019) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest.2019.8644168 · Cited by 87 · see citing works
“For humans, the perceptual difference between frequencies are not linearly proportional to frequency values [14].”
BackgroundHouse, B., Malkin, J., & Bilmes, J. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1518731 · Cited by 83 · see citing works
“Of course, more research is necessary to determine if a simple logscaling is sufficient, or if a more sophisticated pitch-scaling method that models the human perceptual system (such as the mel-scale [16]) would be superior.”
MethodAutomatic Classification of Adventitious Respiratory Sounds: A (Un)Solved Problem?
Rocha, B., Pessoa, D., Marques, A. S. P. D. D., Carvalho, P., & Paiva, R. P. (2020) · Sensors · doi:10.3390/s21010057 · Cited by 82 · see citing works
“The mel scale [47] is a perceptual scale of equally spaced pitches, aiming to match the human perception of sound.”
BackgroundDiscerning Pig Screams in Production Environments
Vandermeulen, J., Bahr, C., Tullo, E., Fontana, I., Ott, S., Kashiha, M., Guarino, M., Moons, C. P. H., Tuyttens, F., Niewold, T., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123111 · Cited by 81 · see citing works
“This experimental scale is used to resemble human perception of sound frequency, particularly fundamental frequency [34].”
MethodA bidimensional model of pitch in the recognition of melodies
Idson, W. L., & Massaro, D. W. (1978) · Perception & Psychophysics · doi:10.3758/bf03198783 · Cited by 81 · see citing works
Room Response Equalization—A Review
Cecchi, S., Carini, A., & Spors, S. (2017) · Applied Sciences · doi:10.3390/app8010016 · Cited by 78 · see citing works
“The mel scale [37], the Bark (critical band rate) scale [38], and the ERB (equivalent rectangular bandwidth) scale [39] are examples of psychoacoustic frequency scales that usually build on a filterbank model of hearing.”
BackgroundThe construction of subjective brightness scales from fractionation data: a validation.
Hanes, R. M. (1949) · Journal of Experimental Psychology · doi:10.1037/h0053962 · Cited by 77 · see citing works
Infant Directed Speech in Natural Interaction—Norwegian Vowel Quantity and Quality
Englund, K. T., & Behne, D. M. (2005) · Journal of Psycholinguistic Research · doi:10.1007/s10936-005-3640-7 · Cited by 74 · see citing works
A method for automated individual, species and call type recognition in free-ranging animals
Mielke, A., & Zuberbühler, K. (2013) · Animal Behaviour · doi:10.1016/j.anbehav.2013.04.017 · Cited by 76 · see citing works
A mechanism of subjective similarity
Eisler, H., & Ekman, G. (1959) · Acta Psychologica · doi:10.1016/0001-6918(59)90080-0 · Cited by 75 · see citing works
The Doppler illusion: The influence of dynamic intensity change on perceived pitch.
Neuhoff, J. G., & McBeath, M. K. (1996) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.22.4.970 · Cited by 73 · see citing works
Emotional sounds of crowds: spectrogram-based analysis using deep learning
Franzoni, V., Biondi, G., & Milani, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09428-x · Cited by 70 · see citing works
Speech Recognition using Dynamic Time Warping
Amin, T. B., & Mahmood, I. (2008) · doi:10.1109/icast.2008.4747690 · Cited by 69 · see citing works
“It is a perceptual scale of pitches judged by listeners to be equal in distance from one another [3].”
BackgroundMethodCognitive Load Reduces Perceived Linguistic Convergence Between Dyads
Abel, J., & Babel, M. (2016) · Language and Speech · doi:10.1177/0023830916665652 · Cited by 67 · see citing works
“Triangular filters are then constructed on a mel scale (Stevens et al., 1937) to give greater weight to lower frequency elements of the spectrum.”
Background
- 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 › Engineering › Aerospace Engineering › 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 ›
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