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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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Dikbıyık, E., Demir, Ö., & Doğan, B. (2025) · IEEE Access · doi:10.1109/access.2025.3559339 · Cited by 14
“Because the Mel frequency scale provides a rough model of human frequency perception, it is widely used to represent audio signals and has extensive applications in the field of audio analysis [46].”
Animal Sounds Classification Scheme Based on Multi-Feature Network with Mixed Datasets
Kim, C.-I., Cho, Y., Jung, S., Rew, J., & Hwang, E. (2020) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2020.08.013 · Cited by 11
“The mels are calculated by Equation (4) which is a formula for converting a frequency f (hertz) into a mel m [26].”
MethodDisentanglement in a GAN for Unconditional Speech Synthesis
Baas, M., & Kamper, H. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3359352 · Cited by 10
“For this we use a pretrained HiFi-GAN vocoder[29]thatvocodeseitherlogmel-scalespectrograms[32] orHuBERTfeatures[18].”
MethodReal-Time Indonesian Language Speech Recognition with MFCC Algorithms and Python-Based SVM
Mustikarini, W., Hidayat, R., & Bejo, A. (2019) · IJITEE (International Journal of Information Technology and Electrical Engineering) · doi:10.22146/ijitee.49426 · Cited by 10
“The mel scale itself is an association between frequency (tone) heard or perceived by humans with actual measured frequency [4].”
BackgroundIdentification of VoIP Speech With Multiple Domain Deep Features
Huang, Y., Li, B., Barni, M., & Huang, J. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2960635 · Cited by 11
“The Mel-scale [37] of a Hertz-scale frequency f is computed by mel ( f ) = 2595log10 ( 1 + f 700 ) , (4) and the inverted Mel-scale frequency [36] by imel ( f ) = mel ( fL) + mel( fH ) − mel ( Fs 2 + Fs N − f ), (5) where fL , fH , and Fs are the lowest frequency, the highest frequency, and the sampling rate, respecti…”
MethodInfluentialDisorders of vocal emotional expression and comprehension: The aprosodias.
Ross, E. (2021) · Handbook of Clinical Neurology · doi:10.1016/b978-0-12-822290-4.00005-0 · Cited by 10
Synthesized Speech Attribution Using The Patchout Spectrogram Attribution Transformer
Bhagtani, K., Bartusiak, E. R., Yadav, A. K. S., Bestagini, P., & Delp, E. J. (2023) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3577163.3595112 · Cited by 12
“The mel-spectrogram is a 2D representation of the speech signal in the time-frequency domain, where frequencies are in mel-scale [27].”
BackgroundDonkin, C., Brown, S. D., & Heathcote, A. (2009) · Behavior Research Methods · doi:10.3758/brm.41.1.154 · Cited by 10
Kattner, F., & Ellermeier, W. (2014) · Attention Perception & Psychophysics · doi:10.3758/s13414-014-0674-7 · Cited by 10
Identifying Aedes aegypti Mosquitoes by Sensors and One-Class Classifiers
Souza, V. M. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-52277-7_2 · Cited by 10
“MFCCs are calculated by taking the magnitudes of frequency components using an acoustically-defined scale called mel [14].”
MethodLanguage and Gender Classification of Speech Files Using Supervised Machine Learning Methods
HaCohen‐Kerner, Y., & Hagege, R. (2017) · Cybernetics & Systems · doi:10.1080/01969722.2017.1383654 · Cited by 10
“The filter is used to extract the MFCC. Bark (Stevens 1937) represents the CC extracted after converting from frequency to the Bark scale.”
MethodUnsupervised blue whale call detection using multiple time-frequency features
Cuevas, A., Veragua, A., Español‐Jiménez, S., et al. (2017) · CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies · doi:10.1109/chilecon.2017.8229663 · Cited by 10
“MFCC is an extension of the ceptrum concept that takes into account the energy of Mel-spaced filter banks [20], thus providing information of the signal according to the Mel scale, where different frequency zones have filter banks of different widths to achieve the desired resolution.”
BackgroundAre Recent Deepfake Speech Generators Detectable?
Bhagtani, K., Yadav, A. K. S., Bestagini, P., & Delp, E. J. (2024) · Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3658664.3659658 · Cited by 9
“D representation of speech obtained using Short Time Fourier Transform (STFT), with time on x-axis and frequency in mel scale on y-axis [6, 7, 35].”
MethodHuang, B., Shao, Y., Zhang, H., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106052 · Cited by 9
An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences
Matt, J. E., Rizzo, D. M., Javed, A., Eppstein, M. J., Manukyan, V., Gramling, C. J., Dewoolkar, A., & Gramling, R. E. (2023) · Journal of Palliative Medicine · doi:10.1089/jpm.2023.0087 · Cited by 10
Scharine, A. A., & McBeath, M. K. (2018) · Auditory Perception & Cognition · doi:10.1080/25742442.2019.1600935 · Cited by 9
Surgical Mask Detection with Deep Recurrent Phonetic Models
Klumpp, P., Arias‐Vergara, T., Vásquez-Correa, J. C., et al. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1723 · Cited by 9
“The first filter-bank was a triangular Mel-bank [15] with 256 bands.”
BackgroundA new fingerprint definition for effective song recognition
Serrano, S., Sahbudin, M. A. B., Chaouch, C., et al. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.06.009 · Cited by 9
Music Genre Classification Using DenseNet and Data Augmentation
Thuy, D. T. L., Loan, T. V., Thành, C. B., & Cuong, N. H. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.036858 · Cited by 9
Learning-Based Auditory Encoding for Robust Speech Recognition
Chiu, Y.-H. B., Raj, B., & Stern, R. M. (2011) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2168209 · Cited by 9
“ditory perception, such as frequency resolution of the cochlea [5], [6], nonlinear compressive effects of the middle ear [7], [8], simultaneous and non-simultaneous masking effects [9]–[12], etc.”
BackgroundFeatures and results of a speech improvement experiment on hard of hearing children
Czap, L., Pintér, J. M., & Varga, E. B. (2018) · Speech Communication · doi:10.1016/j.specom.2018.11.003 · Cited by 9
CNN-based Multimodal Touchless Biometric Recognition System using Gait and Speech
Sarin, S., Mittal, A., Chugh, A., & Srivastava, S. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189765 · Cited by 8
“These spectrograms convert the raw wave-form into an image like structure, so it can be fed into a CNN. Mel scale [41] is a non-linear transformation of frequencies which has been shown to better represent the scale at which humans perceive and differentiate between frequency bands [42].”
Schneider, A. (2018) · Springer handbooks · doi:10.1007/978-3-662-55004-5_30 · Cited by 8
Pipeline monitoring using acoustic principal component analysis recognition with the Mel scale
Wan, C., & Mita, A. (2009) · Smart Materials and Structures · doi:10.1088/0964-1726/18/5/055004 · Cited by 9
“Based on perceptual experiments conducted on humans, Stevens et al [17] proposed the Mel scale as a perceptual scale of pitches judged by listeners to be equal in distance from one another.”
MethodDowling, W. J. (2008) · doi:10.1002/9780470753477.ch15 · Cited by 9
An Explorative, Hierarchical User Interface to Structured Music Repositories
Schedl, M. (2003) · Cited by 9
Emotion Recognition from SpeechPreprint
Venkataramanan, K., & Rajamohan, H. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10458 · Cited by 85
WaveGuard: Understanding and Mitigating Audio Adversarial ExamplesPreprint
Hussain, S., Neekhara, P., Dubnov, S., McAuley, J., & Koushanfar, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03344 · Cited by 41
“The linearly-spaced frequency bins of the resultant spectrogram are then compressed to fewer bins which are equally-spaced on a logarithmic scale (usually the Mel scale [51]).”
BackgroundTransformer-Based Network for Accurate Classification of Lung Auscultation Sounds
Sonali, C. S., Kiran, J., Chinmayi, B. S., Suma, K. V., & Easa, M. (2023) · Critical Reviews in Biomedical Engineering · doi:10.1615/critrevbiomedeng.2023048981 · Cited by 8
Microphone Pair Training for Robust Sound Source Localization With Diverse Array Configurations
An, I., An, G., Kim, T., et al. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3333700 · Cited by 8
“The Mel-spectrogram [13] serves as a useful hand-crafted feature for capturing different characteristics of various sound events.”
BackgroundMultimodal robotic music performance art based on GRU-GoogLeNet model fusing audiovisual perception
Wang, L. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2023.1324831 · Cited by 8
Hoover, K., Chaudhuri, S., Pantofaru, C., Sturdy, I., & Slaney, M. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461891 · Cited by 7
“For the audio pipeline, we experimented with four different front-end feature representations: a Mel filterbank [23] was used to generate the Mel spectra, log-Mel and Mel-Frequency Cepstral Coefficients, and a CARFAC filterbank [24] front-end.”
MethodAuditory stimulus reconstruction from ECoG with DNN and self-attention modules
Senda, J., Tanaka, M., Iijima, K., Sugino, M., Mori, F., Jimbo, Y., Iwasaki, M., & Kotani, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105761 · Cited by 8
Narkthong, N., Duan, S., Ren, S., et al. (2024) · ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620665.3640374 · Cited by 8
“Then, we generate the mel-scale spectogram [47] using 50 ms window, 25 ms window stride, and 10 mel filterbanks per frame.”
MethodMobile Virtual Assistant for Multi-Modal Depression-Level Stratification
Wu, E. H., Gao, T., Chung, C., Chen, C., Tsai, C.-F., & Yeh, S. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3451114 · Cited by 10
“2) Audio: Five spectral features were extracted from the audio file, namely Mel Spectrogram [57], Mel-Frequency Cepstral Coefficients (MFCCs) [58], Spectral Contrast [59], Chroma-gram [60], and Tonal Centroid Features (Tonnetz) [61], using Librosa 8 package.”
Music recommenders: user evaluation without real users?
Craw, S., Horsburgh, B., & Massie, S. (2015) · Cited by 8
Automated Vibrational Signal Recognition and Playback
Korinšek, G., Tuma, T., & Virant‐Doberlet, M. (2019) · Animal signals and communication · doi:10.1007/978-3-030-22293-2_9 · Cited by 8
Multimodal System for Audio Scene Source Counting and Analysis
Nigro, M., & Krishnan, S. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3156795 · Cited by 8
Human-Computer Interaction Through Voice Commands Recognition
Rusan, H.-A., & Mocanu, B. (2022) · International Symposium on Electronics and Telecommunications (ISETC) · doi:10.1109/isetc56213.2022.10010253 · Cited by 8
“In order to extract the Mel spectrograms, we need to convert the linear frequency spectrum into the Mel components (of logarithmic scale) [14] using the following equation: 1124 ⋅ (cid:18) " 700⁄(cid:29) (cid:27)2(cid:29) where f denotes the linear Hertz and m the corresponding Mel coefficient.”
MethodStudy of Applicability of Virtual Users in Evaluating Multimodal Biometrics
Wolf, F., Scheidat, T., & Vielhauer, C. (2006) · Lecture notes in computer science · doi:10.1007/11848035_73 · Cited by 8
“The speaker verification bases upon Mel-Frequency Cepstrum Coefficients (MFCC) using frequency in a logarithmic scale, the mel scale ([13]) and the log spectrum, the cepstrum ([14]) for distinguishing sounds.”
MethodDesign of an efficient music-speech discriminator
Tardón, L. J., Sammartino, S., & Barbancho, I. (2010) · The Journal of the Acoustical Society of America · doi:10.1121/1.3257204 · Cited by 8
The effect of duration on vowel categorization and perceptual prototypes in a quantity language
Eerola, O., Savela, J., Laaksonen, J., & Aaltonen, O. (2012) · Journal of Phonetics · doi:10.1016/j.wocn.2011.12.003 · Cited by 8
Improved voice activity detection for speech recognition system
Chin, S. W., Seng, K. P., Ang, L.-M., et al. (2010) · International Computer Symposium · doi:10.1109/compsym.2010.5685456 · Cited by 8
“By applying the Fourier fast transform (FFT), each of the frames is transformed from time to frequency domain and mapped onto the Mel scale [18].”
MethodPerceptually Based Pitch Scales in Cepstral Techniques for Percussive Timbre Identification
Brent, W. (2009) · Cited by 8
Effects of band reduction and coding on speech emotion recognition
Abas, A., & Lech, M. (2016) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs.2016.7843353 · Cited by 9
“The mel scale [40] is a perceptual scale of pitches judged by listeners to be equal in distance from one another.”
Towards a binaural modelling toolbox
Søndergaard, P., Culling, J. F., Dau, T., et al. (2011) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · Cited by 8
End-to-end let's play commentary generation using multi-modal video representations
Li, C., Gandhi, S., & Harrison, B. E. (2019) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3337722.3341870 · Cited by 8
“To incorporate audio information into the learning process, we first encode the audio stream for each clip as a mel-spectrogram [12, 18].”
MethodOn the ability to directly evaluate sensory ratios
Masin, S. C. (2012) · Attention Perception & Psychophysics · doi:10.3758/s13414-012-0382-0 · Cited by 8
Adaptive recognition of different accents conversations based on convolutional neural network
Zhong, J., Zhang, P., & Li, X. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6590-4 · Cited by 8
“The Mel scale was first proposed by Stevens, Volkman and Newman (1937) [29], the Mel frequency can. be better to represent the sound from the listener ’ s perspective.”
BackgroundForensic comparison of voices, speech and speakers – Tools and Methods in Forensic Phonetics
Lindh, J. (2017) · Gothenburg University Publications Electronic Archive (Gothenburg University) · Cited by 8
Exploring Deep Learning Methods for Discovering Features in Speech Signals
Jaitly, N. (2014) · TSpace (University of Toronto) · Cited by 8
Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps
Dao, T., Sohoni, N. S., Gu, A., Eichhorn, M., Blonder, A., Leszczynski, M., Rudra, A., & Ré, C. (2020) · arXiv · doi:10.48550/arxiv.2012.14966 · Cited by 21
“…(vi) the power spectrum (which has dimension 512) is mapped to the “mel scale” (which is a scale intended to mimic human auditory perception (Stevens et al., 1937)) by multiplication with a specific banded matrix of dimension 512 × 23 , and the entrywise logarithm of the output is taken (the…”
BackgroundWavelet Networks: Scale Equivariant Learning From Raw Waveforms.Preprint
Romero, D. W., Bekkers, E. J., Tomczak, J. M., & Hoogendoorn, M. (2020) · arXiv (Cornell University) · Cited by 10
Dynamic Auditory Remapping Across the Sleep-Wake CyclePreprint
Arzi, A., Trentin, C., Laudini, A., Krugliak, A., Nikolla, D., & Bekinschtein, T. A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.16.431383 · Cited by 7
“The Third model is the Mel Model, obtained from the Mel scale, which indicates how pitch perception evolves as a function of sound frequencies in a non-linear manner (Micheyl et al., 2012; Moore, 2003; Stevens et al., 1937).”
MethodInfluentialCompact Time-Domain Representation for Logical Access Spoofed Audio
Karo, M., Yeredor, A., & Lapidot, I. (2024) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3341000 · Cited by 6
“Some designs are influenced by empirical perceptual frequency scales, such as Mel scale [36] or Equivalent Rectangular Band-width (ERB) scale [37], others are inspired by the human biological hearing system, particularly by the natural design of the human cochlea [38].”
BackgroundHaupt, T., Rosenkranz, M., & Bleichner, M. G. (2025) · eNeuro · doi:10.1523/eneuro.0287-24.2024 · Cited by 10
CREMA-D: Improving Accuracy with BPSO-Based Feature Selection for Emotion Recognition Using Speech
DONUK, K. (2022) · Journal of Soft Computing and Artificial Intelligence · doi:10.55195/jscai.1214312 · Cited by 7
Respiratory sound classification utilizing human auditory-based feature extraction
Rishabh, Kumar, D., Meena, Y., et al. (2025) · Physica Scripta · doi:10.1088/1402-4896/adb80f · Cited by 7
A new multiformat rhythm game for music tutoring
Jaime, J., Barbancho, I., Urdiales, C., et al. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2478-8 · Cited by 7
“In our work, we use the Mel scale to take into consideration the nonlinear perception of frequency by the human auditory system [23].”
MethodEmotional and User-Specific Acoustic Cues for Improved Analysis of Naturalistic Interactions
Siegert, I. (2015) · Künstliche Intell. · doi:10.1007/s13218-015-0394-8 · Cited by 6
“[Stevens et al. 1937]).”
Poirier-Quinot, D., Parseihian, G., & Katz, B. F. G. (2016) · Displays · doi:10.1016/j.displa.2016.05.001 · Cited by 7
Observation oriented modeling revised from a statistical point of view
Sauer, S. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0949-8 · Cited by 8
“An additional problem needs to be considered in some research branches, including most of psychology: Are psychological (i.e., “latent”) attributes additive in structure, that is, are they quantitative (Stevens, Volkmann, & Newman, 1937)?”
BackgroundChild-directed Speech in a Norwegian Kindergarten Setting
Steen, V. B., & Englund, N. (2021) · Scandinavian Journal of Educational Research · doi:10.1080/00313831.2021.1897873 · Cited by 7
“Before statistical analyses, F0–F3 values measured in Hz were recalculated according to the Mel scale, which includes the nonlinearity of the perception of frequency (Stevens et al., 1937).”
MethodBoos, M., Lücke, J., & Rieger, J. W. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118106 · Cited by 6
Speech Audio Cardinal Emotion Sentiment Detection and Prediction Using Deep Learning Approach
Bhardwaj, S., & Sharma, A. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-6448-9_13 · Cited by 7
The Constant-Q Harmonic Coefficients: A timbre feature designed for music signals [Lecture Notes]
Rafii, Z. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3138870 · Cited by 7
Repurposing transfer learning strategy of computer vision for owl sound classification
Gunawan, K. W., Hidayat, A. A., Cenggoro, T. W., & Pardamean, B. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.154 · Cited by 8
Discrimination of mild cognitive impairment based on involuntary changes caused in voice elements
Higuchi, M., Nakamura, M., Omiya, Y., & Tokuno, S. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1197840 · Cited by 7
“Several voice features that were selected during learning were associated with the Mel frequency (29) and MFCC (30).”
BackgroundMdrt: Multi-Domain Synthetic Speech Localization
Yadav, A. K. S., Bhagtani, K., Baireddy, S., Bestagini, P., Tubaro, S., & Delp, E. J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446471 · Cited by 7
Transformer Ensemble for Synthesized Speech Detection
Bartusiak, E. R., Bhagtani, K., Yadav, A. K. S., & Delp, E. J. (2023) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf59524.2023.10477041 · Cited by 8
“The frequencies of the audio signals are in the mel scale, which is a perceptual scale based on the human auditory system [29].”
BackgroundMethodInfluentialCaetano, M., Mouchtaris, A., & Wiering, F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41248-6_10 · Cited by 7
“An interesting analogy is perception of frequencies and the Mel scale [43].”
BackgroundMeasuring the perceptual magnet effect in the perception of /i/ by German listeners
Diesch, E., Iverson, P., Kettermann, A., & Siebert, C. (1999) · Psychological Research · doi:10.1007/s004260050036 · Cited by 8
“Kuhl (1991) justified the isosensitivity assumption by referring to her use of the mel scale ( Stevens, Volkmann, & Newmann, 1937 ) for the measurement of inter-stimulus distances and to arguments and results presented by Fant (1973) and Flanagan (1955, 1957).”
BackgroundSignal adaptive spectral envelope estimation for robust speech recognition
Wölfel, M. (2009) · Speech Communication · doi:10.1016/j.specom.2009.02.006 · Cited by 7
Boucheron, L. E., Leon, P. L. D., & Sandoval, S. (2011) · DCC · doi:10.1109/dcc.2011.17 · Cited by 7
“Finally, we conclude in Section V.”
BackgroundChu, W., & Alwan, A. A. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288298 · Cited by 7
“The Mel-scaled filter ban k is often used for feature extraction in automatic speech recogni ti n (ASR) [6].”
InfluentialInteractive Web-based Image Sonification for the Blind
Wörtwein, T., Schauerte, B., Müller, K., & Stiefelhagen, R. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2823298 · Cited by 7
“The generated sound is then post-processed, for example, with a Hz to mel transformation [2].”
BackgroundStimulus reference frame and neural coding precision
Kostal, L. (2016) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2016.02.006 · Cited by 7
S. S. Stevens, M. Guirao y los estudios psicofísicos en Argentina
Piñeda, M. A., & Sherman, P. (2016) · Revista Mexicana de Análisis de la Conducta · doi:10.5514/rmac.v42.i2.57025 · Cited by 7
Ghasemzadeh, H., & Arjmandi, M. K. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2959222 · Cited by 7
“This is achieved by using the Mel scale, which has been designed based on psychoacoustic experiments [37].”
MethodMood detection analyzing lyrics and audio signal based on deep learning architectures
Pyrovolakis, K., Tzouveli, P. K., & Stamou, G. P. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412361 · Cited by 9
“…between structural features of music and emotions: and emotion [13] In order to achieve an effective audio analysis, we have to adopt some methods [14], [15], [16], [17], [18] from the field of Digital Signal Processing, which make feasible the extraction of features with the desired information.”
MethodAdaptive DCTNet for audio signal classification
Xian, Y., Pu, Y., Gan, Z., Lu, L., & Thompson, A. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952907 · Cited by 6
Sarangi, S. K., & Saha, G. (2020) · Journal of Signal Processing Systems · doi:10.1007/s11265-020-01517-2 · Cited by 7
Improving speech emotion recognition system for a social robot with speaker recognition
Juszkiewicz, Ł. (2014) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2014.6957480 · Cited by 7
“Built in smoothing and interpolation functions are used to overcome this issue. d) Mel-frequency Cepstral Coefficients: (MFCC) are commonly used for parametrisation of the speech [11], [16].”
MethodA Mechanism of Subjective Similarity
Eisler, H., & Ekman, G. (1959) · Nordisk Psykologi · doi:10.1080/00291463.1959.10780403 · Cited by 7
Contour Systems of One Variety of Scottish English
Currie, K. L. (1979) · Language and Speech · doi:10.1177/002383097902200101 · Cited by 7
L'effet de la fréquence fondamentale sur le timbre
Marozeau, J. (2004) · HAL AMU · Cited by 7
Dance Dance ConvolutionPreprint
Donahue, C., Lipton, Z. C., & McAuley, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06891 · Cited by 7
“We reduce the dimensionality of the STFT magnitude spectrum by applying a Mel-scale [41] filterbank yielding 80 frequency bands.”
MethodTime‐Frequency Processing: Spectral Properties
Virtanen, T. I., Vincent, E., & Gannot, S. (2018) · doi:10.1002/9781119279860.ch2 · Cited by 7
“The Mel scale (Stevens et al. , 1937; Makhoul and Cosell, 1976) and the equivalent rectangular bandwidth (ERB) scale (Glasberg and Moore, 1990) are two nonlinear frequency scales motivated by the human auditory system 2) .”
A Two-stage Multi-modal Affect Analysis Framework for Children with Autism Spectrum DisorderPreprint
Li, J., Bhat, A. N., & Barmaki, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09199 · Cited by 9
“A Mel-spectrogram is a spectrogram where the frequencies are converted to the Mel scale - a perceptual scale of pitches assessed by listeners to be equal in distance from one another (Stevens, Volkmann, and Newman 1937).”
Najamuddin, Sheikh, U. U., & Sha’ameri, A. Z. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103570 · Cited by 8
Wang, Z., Zhang, H., Tan, Y., Wang, R., Qian, K., Hu, B., Yamamoto, Y., & Schuller, B. W. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3638846 · Cited by 7
“To accurately model these sound perception mechanisms, psychoacoustic researchers have proposed various frequency scales based on different auditory experimental data, e.g., the Mel scale [10], Bark scale [11], and Equivalent Rectangular Bandwidth (ERB) Scale [12].”
Automatic edition of songs for Guitar Hero/Frets on Fire
Barbancho, A. M., Barbancho, I., Tardón, L. J., et al. (2009) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2009.5202712 · Cited by 6
“In order to take into consideration the nonlinear perception of frequency by the human auditory system, the Mel scale is used [5].”
MethodBouziane, A., Kharroubi, J., & Zarghili, A. (2015) · doi:10.1109/wcitca.2015.7367018 · Cited by 6
“The Mel scale, which was o several hearing based experiments, presented to the speech processing However, various revised approxim presented in the literature afte Stevens[8],[12]–[15].”
MethodInfluentialKonuşma Duygu Tanıma için Akustik Özelliklere Dayalı LSTM Tabanlı Bir Yaklaşım
DONUK, K., & Hanbay, D. (2022) · Computer Science · doi:10.53070/bbd.1113379 · Cited by 6
Zhu, W., Wu, Y., Sun, Z. Y., Shen, W. Z., Guo, G., & Lin, J. (2023) · Theoretical and Applied Mechanics Letters · doi:10.1016/j.taml.2023.100479 · Cited by 6
Xu, A., van Niekerk, D. R., Gerazov, B., Krug, P. K., Birkholz, P., Prom–on, S., Halliday, L. F., & Xu, Y. (2024) · Journal of Phonetics · doi:10.1016/j.wocn.2024.101338 · Cited by 7
Brazilian Vowels Recognition using a New Hierarchical Decision Structure with Wavelet Packet and SVM
Bresolin, A. D. A., Neto, A. D. D., & Alsina, P. J. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366280 · Cited by 6
Towards a model of emotion expression in an interactive robot head
Murray, J. C., Cañamero, L. D., & Hiolle, A. (2009) · IEEE RO-MAN · doi:10.1109/roman.2009.5326131 · Cited by 6
“The speaker recognition system is based on dynamic programming [1] and spectral feature analysis in addition to the common Mel cepstrum analysis [ 19 ], this takes the linear range of frequencies and modifies the normal octaves to fall in to the Mel cepstrum bins, which are based on the natural response of the human e…”
MethodChromas from chromatin: sonification of the epigenome
Cittaro, D., Lazarević, D., & Provero, P. (2016) · F1000Research · doi:10.12688/f1000research.8001.1 · Cited by 6
Mobile Interactive Image Sonification for the Blind
Wörtwein, T., Schauerte, B., Müller, K., & Stiefelhagen, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41264-1_28 · Cited by 6
“Therefore, we use the Mel transformation [22] to have a meaningful auditory representation.”
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
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.
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