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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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Kamble, M. R., & Patil, H. A. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101140 · Cited by 32
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Sinith, M. S., Salim, A., Sankar, K., et al. (2010) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2010.5684389 · Cited by 32
“Thus for each tone with an actual frequency, f, measured in Hz, a subjective pitch is measured on a scale called the ‘mel’ scale [3],[4].”
MethodRobust Features in Deep-Learning-Based Speech Recognition
Mitra, V., Franco, H., Stern, R. M., et al. (2017) · Springer eBooks · doi:10.1007/978-3-319-64680-0_8 · Cited by 32
Primary stimulus generalization as a function of acquisition level in the cat.
Thompson, R. F. (1958) · Journal of Comparative and Physiological Psychology · doi:10.1037/h0042608 · Cited by 32
A Framework for Infrastructure-Free Indoor Localization Based on Pervasive Sound Analysis
Leonardo, R., Barandas, M., & Gamboa, H. F. S. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2817887 · Cited by 31
“The MFCC are based on the mel scale [22], a scale that translates frequencies in hertz to the way humans perceive pitch.”
MethodMusic Genre Classification using Transfer Learning on log-based MEL Spectrogram
Mehta, J., Gandhi, D., Thakur, G., & Kanani, P. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418035 · Cited by 37
“Since humans are not good at perceiving frequencies on a linear scale, a unit of the pitch was proposed by Volkmann and Newman in 1937, called the MEL scale[6], according to which equal distances in pitch sounded equally distant to the listener.”
BackgroundRvachew, S., Alhaidary, A., Mattock, K., et al. (2008) · Journal of Phonetics · doi:10.1016/j.wocn.2008.02.001 · Cited by 31
Lowe, M. L., Ringler, C., & Haws, K. L. (2017) · Appetite · doi:10.1016/j.appet.2017.12.013 · Cited by 36
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Clark, L. (2009) · ERA · Cited by 32
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Hoover, K., Chaudhuri, S., Pantofaru, C., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00079 · Cited by 31
“For the audio pipeline, we experimented with four different front-end feature representations: a Mel filterbank [19] was used to generate the Mel spectra, log-Mel and Mel-Frequency Cepstral Coefficients and a CARFAC filterbank [20] front-end.”
MethodLSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations
Alruwaili, M., Ali, A. H., Almutairi, M., Alsahyan, A., & Mahmood, M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-89651-4 · Cited by 38
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Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems
Horsburgh, B., Craw, S., & Massie, S. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.11.004 · Cited by 31
“To create the MFS texture representation from the frequency spectrum obtained, the spectrum bins k must be grouped into buckets according to the mel scale [34].”
MethodAcoustic Eavesdropping Attacks on Constrained Wireless Device Pairing
Halevi, T., & Saxena, N. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2247758 · Cited by 30
“We speculate that the reason that MFCC features provide better results relative to FFT features is that MFCC use the Mel Scale [30] of frequencies.”
MethodSound Source Distance Estimation Using Deep Learning: An Image Classification Approach
Yiwere, M. Y., & Rhee, E. J. (2019) · Sensors · doi:10.3390/s20010172 · Cited by 31
“The method trains a convolutional recurrent neural network (CRNN) [31,32] using log-scaled mel spectrograms [33] extracted from single-channel audio signals as input features.”
MethodMusic Genre Classification with Transformer Classifier
Zhuang, Y., Chen, Y., & Zheng, J. (2020) · doi:10.1145/3408127.3408137 · Cited by 30
“It compresses the STFT on the frequency axis and optimized for human auditory perception [32] [33].”
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Chadaga, K., Prabhu, S., Vivekananda Bhat, K., Niranjana, S., & Umakanth, S. (2021) · Cogent Engineering · doi:10.1080/23311916.2021.1958666 · Cited by 30
“A phone app (Imran et al., 2020) used the dataset of 48 COVID-19, 131 pertussis, 102 bronchitis and 76 healthy cough recordings to train the model (Stevens et al., 1937).”
MethodDeep transfer learning-based variable Doppler underwater acoustic communications
Liu, Y., Zhao, Y., Gerstoft, P., Zhou, F., Qiao, G., & Yin, J. (2023) · The Journal of the Acoustical Society of America · doi:10.1121/10.0020147 · Cited by 29
Fractionation of temporal intervals.
Gregg, L. W. (1951) · Journal of Experimental Psychology · doi:10.1037/h0061834 · Cited by 30
Fundamental Frequency and Pitch
Hirst, D. J., & Looze, C. D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108644198.014 · Cited by 29
MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
Cheng, H. K., Ishii, M., Hayakawa, A., Shibuya, T., Schwing, A. G., & Mitsufuji, Y. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02691 · Cited by 95
“For this, we first transform audio waveforms via Short-Time Fourier Transform (STFT) and extract mel spectrograms [57], which are then encoded by a pretrained variational autoencoder (VAE) [27] into latents x 1 .”
Lentzen, D., Grutzek, G., Knospe, H., et al. (2011) · International Conference on Communications · doi:10.1109/icc.2011.5963108 · Cited by 29
“MFCC are computed by applying a discrete cosine transform (DCT) to the logarithmic, mel-filtered spectrum that reflects the human ears non-linear frequency perception [12].”
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Georgescu, A.-L., Pappalardo, A., Cucu, H., & Blott, M. (2021) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-021-00217-4 · Cited by 29
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“The ranges of the four mel bands (B1 – B4) have been (0 – 800 mel), (800 – 1600 mel), (1600 – 2400 mel) and beyond (Stevens et al., 1937).”
The Machine Learning Approach for Analysis of Sound Scenes and Events
Heittola, T., Çakır, E., & Virtanen, T. I. (2017) · Springer eBooks · doi:10.1007/978-3-319-63450-0_2 · Cited by 30
“The bins are accumulated into 40 mel bands using non-linear frequency scaling spanning from 0 to 22.05 kHz (assuming audio signal is sampled with 44.1 kHz) and logarithm is applied to get the acoustic features—the log mel band energies [33, 34].”
MethodInfluentialPersonalization of Hearing Aid Compression by Human-in-the-Loop Deep Reinforcement Learning
Alamdari, N., Lobariñas, E., & Kehtarnavaz, N. (2020) · IEEE Access · doi:10.1109/access.2020.3035728 · Cited by 28
“The conversion of frequency f in a STFT-frame to M th Mel-scale frequency is done as follows [39]:”
MethodA Psychoacoustic Engineering Approach to Machine Sound Source Separation in Reverberant Environments
Hummersone, C. (2011) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 29
Group-level Speech Emotion Recognition Utilising Deep Spectrum Features
Ottl, S., Amiriparian, S., Gerczuk, M., Karas, V., & Schuller, B. W. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3417964 · Cited by 29
“To form the Mel-bands, we use 128 filter banks equally spaced on the Mel-scale: This scale used for displaying frequencies is based on the human perception of frequencies that can disitinguish lower frequencies with a higher resolution [31].”
MethodHashmi, M. S. A., Ibrahim, M., Bajwa, I. S., et al. (2022) · Sensors · doi:10.3390/s22051983 · Cited by 28
“It was an attempt to quantify the pitch in a way that reflects the same differences between Mel-scale pitch and perceived pitch, irrespective of the frequency in Hertz [58].”
BackgroundMethodAircraft take-off noises classification based on human auditory’s matched features extraction
Márquez-Molina, M., Fernández, L. P. S., Suárez-Guerra, S., et al. (2014) · Applied Acoustics · doi:10.1016/j.apacoust.2013.12.003 · Cited by 27
Design and evaluation of auditory-supported air gesture controls in vehicles
Sterkenburg, J., Landry, S., & Jeon, M. (2019) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-019-00298-8 · Cited by 28
Speech Breathing Estimation Using Deep Learning Methods
Nallanthighal, V. S., Härmä, A., & Strik, H. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053753 · Cited by 30
“Mel filter banks use Mel-frequency scaling, which is a perceptual scale to replicate human ear perception of sound [11].”
BackgroundParrell, B., Kim, H. E., Breska, A., Saxena, A., & Ivry, R. B. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.0739-21.2021 · Cited by 26
Contemporary Methods for Speech Parameterization
Ganchev, T. D. (2011) · Springer eBooks · doi:10.1007/978-1-4419-8447-0 · Cited by 27
Leveraging Multimodal Haptic Sensory Data for Robust Cutting
Zhang, K., Sharma, M. K., Veloso, M., & Kroemer, O. (2019) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids43949.2019.9035073 · Cited by 28
“The Mel frequency scale provides a rough model of human frequency perception [23].”
BackgroundLiang, S. H. L., Saeedi, S., Ojagh, S., Honarparvar, S., Kiaei, S., Jahromi, M. M., & Squires, J. (2020) · Sensors · doi:10.3390/s21010050 · Cited by 27
“The most commonly known time-frequency feature is the shorttime Fourier transform [67], Mel spectrogram [68], and wavelet spectrogram [69].”
BackgroundA Music Classification model based on metric learning applied to MP3 audio files
da Silva, A. C. M., Coelho, M. A. N., & Neto, R. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113071 · Cited by 27
Singing voice analysis/synthesis
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Inductive biases, pretraining and fine-tuning jointly account for brain responses to speechPreprint
Millet, J., & King, J.-R. (2021) · doi:10.31219/osf.io/fq6gd · Cited by 26
“They thus provide a reasonable approximation of the features input to the primary auditory cortex (Stevens et al., 1937; Stevens & Volkmann, 1940).”
BackgroundInvestigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones
Pillai, A., Nepal, S. K., Wang, W., Nemesure, M. D., Heinz, M. V., Price, G. D., Lekkas, D., Collins, A. C., Griffin, T. Z., Buck, B., et al. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631452 · Cited by 26
Gunawan, K. W., Hidayat, A. A., Cenggoro, T. W., & Pardamean, B. (2021) · International Journal on Electrical Engineering and Informatics · doi:10.15676/ijeei.2021.13.3.3 · Cited by 25
An Overview of Recent Work in Multimedia Forensics
Bhagtani, K., Yadav, A. K. S., Bartusiak, E. R., Xiang, Z., Shao, R., Baireddy, S., & Delp, E. J. (2022) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr54900.2022.00064 · Cited by 25
“A melspectrogram refers to a spectrogram with frequencies in the mel scale [56].”
FamilyLog: A mobile system for monitoring family mealtime activities
Bi, C., Xing, G., Tian, H., Huh, J., Peng, W., & Ma, M. (2017) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom.2017.7917847 · Cited by 25
“Then the resulting spectrum is transformed into 21 energy channels by applying Mel Filters [21][25][30].”
MethodSpoken Digit Recognition in Portuguese Using Line Spectral Frequencies
Silva, D. F., Souza, V. M. A., Batista, G. E. A. P. A., et al. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34654-5_25 · Cited by 25
Prosodic prominence effects on vowels in chain shifts
Jacewicz, E., Fox, R. A., & Salmons, J. (2006) · Language Variation and Change · doi:10.1017/s0954394506060145 · Cited by 25
Ward, W. D. (1970) · Elsevier eBooks · doi:10.1016/b978-0-12-691901-1.50016-4 · Cited by 25
Huang, C.-W., & Narayanan, S. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02901 · Cited by 24
“The commonly used features include pitch, log-Mel filterbank energies (log-Mels) [2], Mel-frequency cepstral coefficients (MFCCs) [3] and perceptual linear prediction in the acoustic modality, and Haar, local binary pattern, histogram of oriented gradients and scale-invariant feature transform in…”
MethodPerformance Evaluation of Front-end Processing for Speech Recognition Systems
Cheng, O., Abdulla, W. H., & Salcic, Z. A. (2005) · Cited by 24
A comparison between audio and IMU data to detect chewing events based on an earable device
Lotfi, R., Tzanetakis, G., Eskicioglu, R., & Irani, P. (2020) · Proceedings of the 11th Augmented Human International Conference · doi:10.1145/3396339.3396362 · Cited by 24
“…as follows: (1) Partition the signal into short frames (3 seconds) (2) Calculate the Discrete Fourier Transform for each frame (3) Apply mel filter bank [27] with 45 filters on magnitude spectrum obtained from step 2 and extract a 45 dimensional feature vector (4) Take the logarithm of all…”
MethodTBscreen: A passive cough classifier for tuberculosis screening with a controlled dataset
Sharma, M., Nduba, V., Njagi, L. N., Murithi, W. B., Mwongera, Z., Hawn, T. R., Patel, S., & Horne, D. J. D. L. (2024) · Science Advances · doi:10.1126/sciadv.adi0282 · Cited by 26
“These filter banks mimic frequency sensitivity of a human ear as well reduces dimension of an STFT-based spectrogram ( 66 ).”
BackgroundWu, X., Wellington, S., Fu, Z., & Zhang, D. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad593a · Cited by 24
“The mel-spectrogram representation was calculated using a short-term Fourier transform with a window of size 50 ms and a frameshift of 10 ms [54].”
MethodUlises: A Agent-Based System For Timbre Classification
Teixeira, E. P., Gonçalves, E. M. N., & Adamatti, D. F. (2017) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij2017623344 · Cited by 23
A New Acoustic-Based Pronunciation Distance Measure
Bartelds, M., Richter, C., Liberman, M., & Wieling, M. B. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00039 · Cited by 23
“Instead, the Mel scale is composed such that the frequencies below 1,000 Hz are approximately linearly spaced, and the frequencies above 1,000 Hz are distributed according to a logarithmic scale (Stevens et al., 1937).”
BackgroundHeart Valve Diseases Detection Based on Feature-Fusion and Hierarchical LSTM Network
Das, S., Jyotishi, D., & Dandapat, S. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3210961 · Cited by 23
“Third, K number of triangular filter banks are used in mel-scale to uniformly divide the entire frequency band of each frame [32].”
MethodHuman interaction categorization by using audio-visual cues
Marín-Jiménez, M. J., Muñoz‐Salinas, R., Yeguas-Bolívar, E., et al. (2013) · Machine Vision and Applications · doi:10.1007/s00138-013-0521-1 · Cited by 23
Speech Emotion Recognition: A Review
Krothapalli, S. R., & Koolagudi, S. G. (2012) · Springer briefs in electrical and computer engineering · doi:10.1007/978-1-4614-5143-3_2 · Cited by 25
Speech-Signal-Based Frequency Warping
Paliwal, K. K., Shannon, B. J., Lyons, J., et al. (2009) · IEEE Signal Processing Letters · doi:10.1109/lsp.2009.2014096 · Cited by 23
“Its discrete-time short-time Fourier transform is given by (1) where is a samples long analysis window, is the sampling frequency and denotes the continuous frequency variable.”
BackgroundWhat does Chao have to say about tones
Fon, J., & Chiang, W.-Y. (1999) · Journal of Chinese linguistics · Cited by 22
ABX-Discriminability Measures and Applications
Schatz, T. (2016) · theses.fr (ABES) · Cited by 23
Petrosyan, A., Voskoboinikov, A., Sukhinin, D., Makarova, A. V., Skalnaya, A. A., Arkhipova, N., Sinkin, M. V., & Ossadtchi, A. E. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/aca1e1 · Cited by 26
“Audio data were processed us122 ing librosa Python software package [36] in order to extract log-mel spectral coefficients (LMSC) [50], mel-frequency 123 cepstral coefficients (MFCC) [57] and several derivatives of the linear predictive coding (LPC) coefficients as de124 scribed below.”
MethodBrain responses to spoken F0 changes: Is H special?
Hsu, C.-H., Evans, J. P., & Lee, C. (2015) · Journal of Phonetics · doi:10.1016/j.wocn.2015.02.003 · Cited by 21
From raw audio to a seamless mix: creating an automated DJ system for Drum and Bass
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“For features, the loudness [29] of each beat fragment and the energy distribution of the audio along the frequency axis, binned in 12 equally spaced bins on the Mel frequency scale [28], are calculated.”
MethodSergeant, D. (1983) · Psychology of Music · doi:10.1177/0305735683111001 · Cited by 22
Angrick, M., Luo, S., Rabbani, Q., Candrea, D. N., Shah, S., Milsap, G. W., Anderson, W. S., Gordon, C. R., Rosenblatt, K. R., Clawson, L., et al. (2023) · doi:10.1101/2023.06.30.23291352 · Cited by 95
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“Stevens et al. (1937) constructed the original mel scale using the method of fractionation.”
MethodEvaluating raw waveforms with deep learning frameworks for speech emotion recognition
Kilimci, Z. H., Bayraktar, Ü., & Küçükmanіşa, A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20930-y · Cited by 35
“Mel-scaled spectrogram is widely applied in sound classification and speech emotion recognition tasks [ 51].”
Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices
López‐Espejo, I., Tan, Z., & Jensen, J. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2984089 · Cited by 19
“Since we are also interested in phase difference information, a widespread perceptually-motivated filter bank for speech signals like the Mel-scale filter bank [35], defined only for spectral magnitudes, is not a good choice.”
BackgroundMartinsson, J., Runefors, M., Frantzich, H., Glebe, D., McNamee, M., & Mogren, O. (2022) · Fire Technology · doi:10.1007/s10694-022-01307-1 · Cited by 22
“The time-frequency representation is a Mel spectrogram [31] which is a series of short-time Fourier transforms on sequences of the input data followed by a Mel filter-bank which projects them onto Mel bins.”
MethodZipf's Law in Short-Time Timbral Codings of Speech, Music, and Environmental Sound Signals
Haro, M., Serrà, J., Herrera, P., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033993 · Cited by 21
“This suggests that similar results would be obtained for other psychoacoustical scales like the Mel scale [43] or the ERB scale [44].”
ResultDesign and Evaluation of a Sound Based Water Flow Measurement System
Ibarz, A., Bauer, G., Casas, R., et al. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88793-5_4 · Cited by 21
“In order to reduce the spectrum’s size we map the FFT’s frequency values to the “mel scale” using 31 triangular overlapping windows following a logarithmic pattern [ 10 ], a high number of features were obtained to keep good frequency resolution.”
MethodMulti-band long-term signal variability features for robust voice activity detection
Tsiartas, A., Chaspari, T., Katsamanis, N., et al. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-201 · Cited by 21
Procedural Content Generation of Rhythm Games Using Deep Learning Methods
Yubin, L., Li, W., & Ikeda, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34644-7_11 · Cited by 21
The Impact of Audio Input Representations on Neural Network based Music Transcription
Cheuk, K. W., Agres, K. R., & Herremans, D. (2020) · doi:10.1109/ijcnn48605.2020.9207605 · Cited by 21
“3) MelSpec: Mel spectrograms use a Mel scale which is an attempt to imitate humans’ perceptual hearing on musical pitches [19, 20, 21].”
MethodPerceptual Properties of Current Speech Recognition Technology
Heřmanský, H., Cohen, J. R., & Stern, R. M. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2252316 · Cited by 21
“Early experiments [27] revealed a logarithmic-like dependency of perceived pitch on frequency above about 800 Hz.”
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A Machine Learning approach for Early Detection of Parkinson’s Disease Using acoustic traces
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Musical Instrument Sound Morphing Guided by Perceptually Motivated Features
Caetano, M., & Xavier Rodet, A. (2013) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2013.2260154 · Cited by 20
“In this work, we use the mel scale [39] to warp the frequency axis and logarithmic amplitude to better represent loudness perception.”
MethodDetecting Music Genre Using Extreme Gradient Boosting
Murauer, B., & Specht, G. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191822 · Cited by 21
“Thereby, the raw frequency is normalized to the mel scale, which more accurately represents a listeners perceived frequency [15].”
Houtsma, A. J. M. (1995) · Elsevier eBooks · doi:10.1016/b978-012505626-7/50010-8 · Cited by 20
A construction and attempted validation of sensory sweetness scales.
MacLeod, S. (1952) · Journal of Experimental Psychology · doi:10.1037/h0057860 · Cited by 20
Tool Condition Monitoring for milling process using Convolutional Neural Networks
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Improved audio features for large-scale multimedia event detection
Metze, F., Rawat, S., & Wang, Y. (2014) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2014.6890234 · Cited by 19
Amplitude and Frequency Modulation-based features for detection of replay Spoof Speech
Kamble, M. R., Tak, H., & Patil, H. A. (2020) · Speech Communication · doi:10.1016/j.specom.2020.10.003 · Cited by 19
Speech information retrieval: a review
Hafen, R., & Henry, M. J. (2012) · Multimedia Systems · doi:10.1007/s00530-012-0266-0 · Cited by 19
“There are two popular frequency scales: the mel scale [108] and the bark scale [133].”
BackgroundBarker, T., & Virtanen, T. I. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2602546 · Cited by 19
“The frequency resolution varies in different representations, but the spectrogram (alternatively called short-time Fourier transform or STFT), is popular, along with the perceptually motivated mel-band [8] and constant-Q [9] scalings.”
BackgroundSendra, A. L., Gómez-Bellido, J., Carrasco, A., et al. (2018) · Sensors · doi:10.3390/s18061803 · Cited by 19
Audio Content Descriptors of Timbre
Caetano, M., Saitis, C., & Siedenburg, K. (2019) · Springer handbook of auditory research · doi:10.1007/978-3-030-14832-4_11 · Cited by 19
“(1937) concluded that the mel scale captures the concept of pitch height (i.e., higher or lower pitches) as opposed to pitch chroma (i.e., the octave-independent musical notes).”
BackgroundAccent classification in human speech biometrics for native and non-native english speakers
Bird, J. J., Wanner, E. F., Ekárt, A., & Faria, D. R. (2019) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3316782.3322780 · Cited by 20
“MFCC datasets are produced from a sliding time window as follows: (1) The Fourier Transform (FT) of the time window data is derived: (2) The powers from the FT are mapped to the mel-scale, which is the psychological scale of audible pitch[29].”
MethodAutomatic Segmentation of Audio Signals for Bird Species Identification
Evangelista, T. L. F., Priolli, T. M., Junior, C. N. S., et al. (2014) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2014.46 · Cited by 19
“[33] that relates physical frequencies to the frequencies that are perceived by the human ear; this scale has been used in several applications involving speech and music.”
MethodProsodic stress: Acoustic, aphasic, aprosodic and neuroanatomic interactions
Ross, E. D., Shayya, L., & Rousseau, J. F. (2013) · Journal of Neurolinguistics · doi:10.1016/j.jneuroling.2013.02.003 · Cited by 19
Prestressing wire breakage monitoring using sound event detection
Farhadi, S., Corrado, M., Borla, O., & Ventura, G. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13079 · Cited by 21
“Mel is an abbreviation of melody and a measurement unit based on human auditory perception (Stevens & Volkmann, 1940; Stevens et al., 1937).”
BackgroundDeveloping Clinically Relevant Scales of Breathy and Rough Voice Quality
Eddins, D. A., Anand, S., Lang, A., et al. (2020) · Journal of Voice · doi:10.1016/j.jvoice.2019.12.021 · Cited by 18
Efficient GAN - Based Unsupervised Anomaly Sound Detection for Refrigeration Units
Hatanaka, S., & Nishi, H. (2021) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576445 · Cited by 20
“Using the Mel Scale, the difference in height and frequency perceived by humans becomes the same [9].”
MethodA Spatial–Temporal Graph Model for Pronunciation Feature Prediction of Chinese Poetry
Wang, Q., Liu, W., Wang, X., et al. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3165554 · Cited by 18
Abbasi, R., Balázs, P., Marconi, M. A., Nicolakis, D., Zala, S. M., & Penn, D. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010049 · Cited by 19
Speech emotion recognition for the Urdu language
Zaheer, N., Ahmad, O. U., Shabbir, M., & Raza, A. A. (2022) · Language Resources and Evaluation · doi:10.1007/s10579-022-09610-7 · Cited by 18
“They vary from using Mel-frequency Cepstral Coefficients (MFCC) (Sahidul-lah & Saha, 2012) to using Mel-frequency spectrogram (Stevens et al., 1937).”
MethodLearning Neural Audio Embeddings for Grounding Semantics in Auditory Perception
Kiela, D., & Clark, S. C. (2017) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5665 · Cited by 18
Optimisation of phonetic aware speech recognition through multi-objective evolutionary algorithms
Bird, J. J., Wanner, E. F., Ekárt, A., & Faria, D. R. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113402 · Cited by 19
“The powers from the FT are mapped to the Mel scale, the psychological scale of audible pitch (Stevens et al., 1937).”
Method
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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