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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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Duifhuis, H. (1972) · TU/e Research Portal · doi:10.6100/ir115512 · Cited by 18
Manohar, K., Jayan, A. R., & Rajan, R. (2023) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-023-00313-7 · Cited by 17
“MFCCs are commonly used as features in acoustic modeling due to their effectiveness in capturing the phonetic information necessary for speech recognition tasks [53].”
BackgroundDeveloping children's ASR system under low-resource conditions using end-to-end architecture
Ankita, & Shahnawazuddin, S. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104385 · Cited by 17
Scalable Music: Automatic Music Retargeting and Synthesis
Wenner, S., Bazin, J., Sorkine‐Hornung, A., et al. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12054 · Cited by 17
Development of frequency discrimination at 250 Hz is similar for tone and /ba/ stimuli
Buss, E., Flaherty, M. M., & Leibold, L. J. (2017) · The Journal of the Acoustical Society of America · doi:10.1121/1.4994687 · Cited by 17
On Training Targets and Objective Functions for Deep-learning-based Audio-visual Speech Enhancement
Michelsanti, D., Tan, Z., Sigurdsson, S. O., et al. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682790 · Cited by 17
“To incorporate the fact that the human auditory system is more discriminative at low than at high frequencies [19], a Mel-scaled spectrum may be defined as Al = BAl, where Al denotes an F dimensional vector of STFT coefficient magnitudes for time frame l, and B ∈ RQ×F is a matrix, implementing a Mel-spaced filter bank…”
BackgroundExpediting TTS Synthesis with Adversarial Vocoding
Neekhara, P., Donahue, C., Puckette, M., Dubnov, S., & McAuley, J. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3099 · Cited by 17
“The linearly-spaced frequency bins of the resultant spectrogram are then compressed to fewer bins which are equallyspaced on a logarithmic scale (usually the mel scale [13]).”
BackgroundAtkar, G. B., & Jayaraju, P. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05695-3 · Cited by 17
“From previous examples, WaveGAN achieves more inception score around 6.0 [25].”
MethodInfluentialRobust speech recognition using a Small Power Boosting algorithm
Kim, C., Kumar, K., & Stern, R. M. (2009) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2009.5373230 · Cited by 17
“The only differenc e between the conventional MFCC features and our features is the use of the gammatone-shaped frequency integration with the equivalent rectangular bandwidth (ERB) scale [18] instead of the triangular integration with with the MEL scale [ 19 ].”
MethodConsistency influences altered auditory feedback processing
Franken, M. K., Acheson, D. J., McQueen, J. M., Peter Hagoort, A., & Eisner, F. (2019) · Quarterly Journal of Experimental Psychology · doi:10.1177/1747021819838939 · Cited by 17
Über ein Äquivalenzgesetz für Intervalle akustischer Empfindungsgrö\en
Terhardt, E. (1968) · Kybernetik · doi:10.1007/bf00271246 · Cited by 17
Müller, N., Reermann, J., & Meisen, T. (2024) · IEEE Access · doi:10.1109/access.2024.3480788 · Cited by 21
“As humans do not perceive sound linearly, the logarithmic Mel-Scale is often applied to mimic the human auditory perception [66] in the form of a Log Mel Spectrogram (LMS).”
MethodMMFeat: A Toolkit for Extracting Multi-Modal Features
Kiela, D. (2016) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/p16-4010 · Cited by 15
“In MFCC, frequency bands are spaced along the mel scale (Stevens et al., 1937), which has the advantage that it approximates human auditory perception more closely than e.g. linearly-spaced frequency bands.”
BackgroundBird, J. J., Faria, D. R., Premebida, C., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341557 · Cited by 16
Synthesized Speech Detection Using Convolutional Transformer-Based Spectrogram Analysis
Bartusiak, E. R., & Delp, E. J. (2021) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf53345.2021.9723142 · Cited by 20
“For these methods, the speech waveforms are converted to sequences of coefficients – specifically Constant Q Cepstral Coefficients (CQCCs) [15] or Mel Frequency Cepstral Coefficients (MFCCs) [16]–[18] – and analyzed by the various neural networks.”
BackgroundMethodHarmonic Attention for Monaural Speech Enhancement
Wang, T., Zhu, W., Gao, Y., et al. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3284522 · Cited by 16
“Mel scale [41] and equivalent rectangular bandwidth (ERB) mapping [42] are often used to compress high-frequency information in modeling.”
BackgroundIm, S., Kim, T., Min, C., Kang, S., Roh, Y., Kim, C., Kim, M., Kim, S. H., Shim, K., Koh, J., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0294447 · Cited by 16
Consciousness Under the Spotlight: The Problem of Measuring Subjective Experience
Jimenez, M., Prieto, A., Hinojosa, J. A., & Montoro, P. R. (2024) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1697 · Cited by 20
FamilyLog: Monitoring Family Mealtime Activities by Mobile Devices
Bi, C., Xing, G., Tian, H., Huh, J., Peng, W., Ma, M., & Chang, X. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2019.2916357 · Cited by 17
“Then the resulting spectrum is transformed into 21 energy channels on the frequency domain by applying the Mel Filters [30], [31], as shown in Fig.”
MethodLiu, J., Wang, C., & Zha, L. (2021) · Electronics · doi:10.3390/electronics10182206 · Cited by 16
“Stanley Smith Stevens, John Volkman, and Newman named it [30] based on the definition of”
BackgroundInterpreting expressive performance through listener judgments of musical tension
Farbood, M. M., & Upham, F. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00998 · Cited by 16
A novel approach in feature level for robust text-independent speaker identification system
Sarangi, S. K., & Saha, G. (2012) · doi:10.1109/ihci.2012.6481824 · Cited by 16
“They are mel scale [2] and bark scale [3].”
BackgroundUsing Enhanced Cepstral Analysis for Structural Health Monitoring
Ferraris, M., Civera, M., Ceravolo, R., et al. (2019) · Lecture notes in mechanical engineering · doi:10.1007/978-981-13-8331-1_11 · Cited by 16
An Audio Scene Classification Framework with Embedded Filters and a DCT-based Temporal Module
Chen, H., Zhang, P., & Yan, Y. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683636 · Cited by 16
“Most features are based on short-time Fourier transform (STFT), for example the Mel-scale filter bank (FBank) [3].”
MethodOn the Origin of “Scales of Measurement”
Newman, E. B. (1974) · Springer eBooks · doi:10.1007/978-94-010-2245-3_12 · Cited by 16
Self-citationFeatures Based on Auditory Physiology and Perception
Stern, R. M., & Morgan, N. H. (2012) · doi:10.1002/9781118392683.ch8 · Cited by 16
Invariants in auditory frequency relations
Hahn, J., & Jones, M. R. (1981) · Scandinavian Journal of Psychology · doi:10.1111/j.1467-9450.1981.tb00388.x · Cited by 16
Modulating STDP With Back-Propagated Error Signals to Train SNNs for Audio Classification
Peterson, D. G., Nawarathne, T., & Leung, H. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3174905 · Cited by 15
“This transformation is based on human perception of pitch, and it also reduces the time di-mension of the input sequence significantly [34].”
BackgroundAutomatic breach detection during spine pedicle drilling based on vibroacoustic sensing
Massalimova, A., Timmermans, M., Cavalcanti, N., et al. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102641 · Cited by 15
Automatic speaker and age identification of children from raw speech using sincNet over ERB scale
Radha, K., Bansal, M., & Pachori, R. B. (2024) · Speech Communication · doi:10.1016/j.specom.2024.103069 · Cited by 15
The effect of intensity on relative pitch
Thompson, W. F., Peter, V., Olsen, K. N., & Stevens, C. (2012) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2012.678369 · Cited by 15
“Stevens, Volkmann, and Newman (1937) examined whether the relation between pitch and frequency was strictly logarithmic and used psychophysical scaling strategies to derive a new scale, called the mel scale.”
BackgroundMethodEmotion recognition from speech using prosodic features
Väyrynen, E. (2014) · Cited by 15
Predicting Content Similarity via Multimodal Modeling for Video-In-Video Advertising
Song, X., Xu, B., & Jiang, Y. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2979928 · Cited by 15
“An approximate mathematical conversion can be performed between the Mel scale and the linear frequency scale Hertz (Hz) [33].”
MethodPriva, U. C., & Sanker, C. (2020) · Cognitive Science · doi:10.1111/cogs.12897 · Cited by 17
A Computationally Efficient Sound Environment Classifier for Hearing Aids
Gil‐Pita, R., Ayllón, D., Ranilla, J., et al. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2427452 · Cited by 15
“Mel frequency scale [9], and they are defined according to”
BackgroundPitch adaptive MFCC features for improving children’s mismatched ASR
Ghai, S., & Sinha, R. (2015) · International Journal of Speech Technology · doi:10.1007/s10772-015-9291-7 · Cited by 15
“Since the mel scale has approximately a linear frequency spacing below 1 kHz and a logarithmic spacing above 1 kHz (Stevens et al. 1937), the filters up to 1 kHz have narrow bandwidths while the bandwidths of filters above 1 kHz increase log-arithmically.”
BackgroundTu, F., Zhang, T. Y., Liu, T., Zhang, D., & Yang, S. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551919 · Cited by 16
“The Mel scale, introduced by Stanley Smith Stevens in the 1950s, was designed to explore the relationship between pitch and audio frequency in human auditory perception [28].”
Birdsong classification based on multi feature channel fusion
Liu, Z., Chen, W., Chen, A., et al. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12570-3 · Cited by 14
“Stevens et al. [31] considered the sensitivity of human hearing to birdsong comprehensively, it is more suitable to visualize the birdsong audio signal as LMS.”
BackgroundPsychoacoustic Foundations of Major-Minor Tonality
Parncutt, R. (2024) · The MIT Press eBooks · doi:10.7551/mitpress/15050.001.0001 · Cited by 15
Improved Haptic Music Player with Auditory Saliency Estimation
Hwang, I., & Choi, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44193-0_30 · Cited by 14
Speech emotion recognition cross language families: Mandarin vs. western languages
Xiao, Z., Wu, D., Zhang, X., et al. (2016) · doi:10.1109/pic.2016.7949505 · Cited by 14
“Mel-Frequency Cepstrum Coefficients (MFCC) are obtained from a spectrum filtered by Mel scale [20], which is inspired by the characteristics of human perception.”
MethodA Universal System for Cough Detection in Domestic Acoustic Environments
Simou, N., Stefanakis, N., & Zervas, P. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287659 · Cited by 14
“Results in this paper are shown in terms of two classifiers, a DNN accepting log-mel energies [15] as input and a SVM classifier operating on a selection of hand-crafted acoustic features that have already been proposed in the context of cough detection.”
MethodLearning-based auditory encoding for robust speech recognition
Chiu, Y.-H. B., Raj, B., & Stern, R. M. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495666 · Cited by 14
“It is often hypothesized that the various features of human sound perception, such as the frequency resolution of the cochlea [3], nonlinear compressive effects of the middle ear [4], simultaneous and temporal masking effects [5] etc.”
BackgroundCategory labels induce boundary-dependent perceptual warping in learned speech categories
Tummeltshammer, K. S., & Myers, E. B. (2013) · Second language Research · doi:10.1177/0267658313491763 · Cited by 14
“This latter difference seems to indicate an asymmetry in the same direction as the behavioral results; however, transforming these values into the Mel scale (Stevens et al., 1937) to more closely correspond with human pitch discrimination acuity reveals more similar values (the difference in onset frequency of F3 is 1…”
ResultThe Structure and Acquisition of Sensorimotor Maps
van Vugt, F. T., & Ostry, D. J. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01204 · Cited by 17
“In order to normalise the spaceto-frequency mapping, angles were mapped linearly to frequencies in mel-space, which is an intervalscale encoding of frequency differences (Stevens, 1937).”
MethodTechniques for modelling Phonological Processes in Automatic Speech Recognition
Nock, H. J. (2001) · Cited by 13
Distributed Learning of Deep Neural Networks using Independent Subnet TrainingPreprint
Yuan, B., Wolfe, C. R., Dun, C., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.02120 · Cited by 14
“We represent each waveform as a 4096-dimensional feature vector [67].”
MethodAutomatic Behavior Assessment from Uncontrolled Everyday Audio Recordings by Deep Learning
Schindler, D., Spors, S., Demiray, B., & Krüger, F. (2022) · Sensors · doi:10.3390/s22228617 · Cited by 10
“CNNs for audio classification are typically applied to spectral representations of audio signals [8,10,11], such as the Mel-spectrogram based on the Mel-Scale [12], created to capture human audio perception.”
Movement variability can be modulated in speech production
Tang, D.-L., Parrell, B., & Niziolek, C. A. (2022) · Journal of Neurophysiology · doi:10.1152/jn.00095.2022 · Cited by 14
“It should be noted that all formant frequency alterations were applied in mels, a logarithmic transformation of a signal’s frequency such that equal differences in mels are judged by listeners to correspond to equal changes in pitch (19).”
BackgroundPark, Y., Anand, S., Gifford, S. M., et al. (2022) · Journal of Speech Language and Hearing Research · doi:10.1044/2022_jslhr-22-00280 · Cited by 13
Effective MLP and CNN based ensemble learning for speech emotion recognition
Middya, A. I., Nag, B., & Roy, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19017-x · Cited by 13
Speech emotion recognition systems and their security aspects
Gurowiec, I., & Nissim, N. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10760-z · Cited by 13
Karaaslan, M., Türkoğlu, B., Kaya, E., et al. (2024) · Sensors · doi:10.3390/s24247978 · Cited by 13
Speech Emotion Recognition based on Optimized Support Vector Machine
Yu, B., Li, H., & Fang, C. (2012) · Journal of Software · doi:10.4304/jsw.7.12.2726-2733 · Cited by 13
“Therefore for each tone with an actual frequency f measured in Hz, a subjective pitch is measured on a scale called the Mel scale [14].”
MethodText-dependent Speaker Recognition by Combination of LBG VQ and DTW for Persian Language
KeshavarzBahaghighat, M., Sahba, F., & Tehrani, E. S. (2012) · International Journal of Computer Applications · doi:10.5120/8126-1711 · Cited by 13
“The Mel scale, named by [21] is a perceptual scale of pitches judged by listeners to be equal in distance from one another.”
BackgroundQuantifying group specificity of animal vocalizations without specific sender information
Vester, H., Hammerschmidt, K., Timme, M., et al. (2016) · Physical review. E · doi:10.1103/physreve.93.022138 · Cited by 13
Speech and Audio Processing: A MATLAB®-based Approach
McLoughlin, I. V. (2016) · Kent Academic Repository (University of Kent) · Cited by 13
Bird, J. J., Faria, D. R., Premebida, C., Ekárt, A., & Ayrosa, P. P. S. (2020) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc49921.2020.9096166 · Cited by 16
“The MFCC extraction process is as follows: 1) The Fourier Transform (FT) of the time window data ω is calculated: 2) The powers from the FT are mapped to the Mel scale, the psychological scale of audible pitch [42] via a triangular temporal window.”
MethodEnvironmental Noise Classification with Inception-Dense Blocks for Hearing Aids
Ting, P.-J., Ruan, S.-J., & Li, L. P. (2021) · Sensors · doi:10.3390/s21165406 · Cited by 13
“The function B for computing the m th mel-frequency from frequency f in Hertz and its inverse B − 1 are given by [24]: B − 1 ( m ) = 700 ( 10 m 2595 − 1 ) .”
MethodThe phonetic footprint of Parkinson’s disease
Klumpp, P., Arias‐Vergara, T., Vásquez-Correa, J. C., Pérez-Toro, P. A., Orozco‐Arroyave, J. R., Batliner, A. M., & Nöth, E. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101321 · Cited by 17
“Both spectrograms were converted to logarithmic scale of base 10 and afterwards filtered with a triangular Mel-bank [30] with 128 frequency bands to resemble the human perception of speech with a high resolution in the lower frequencies and a coarse resolution for high frequencies.”
Bordeau, C., Scalvini, F., Migniot, C., Dubois, J., & Ambard, M. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1079998 · Cited by 12
“We used theMel scale (Stevens et al., 1937) to take into account the perceived scaling in sound frequency discrimination.”
MethodPersonality trait estimation in group discussions using multimodal analysis and speaker embedding
Mawalim, C. O., Okada, S., Nakano, Y., & Unoki, M. (2023) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-023-00401-0 · Cited by 14
“An MFCC can be obtained by mapping the Fourier power spectrum of a signal onto the Mel scale [48].”
MethodDirection of arrival estimation of an acoustic wave using a single structural vibration sensor
DiPassio, T., Heilemann, M. C., & Bocko, M. F. (2023) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2023.117671 · Cited by 13
Audio2Gestures: Generating Diverse Gestures From Audio
Li, J., Kang, D., Pei, W., Zhe, X., Zhang, Y., Bao, L., & He, Z. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3276973 · Cited by 14
“Trinity Dataset: The audio data are resampled to 16 kHz for extracting log-mel spectrogram [52] feature using librosa [53].”
MethodMusic Performers Classification by Using Multifractal Features: A Case Study
Reljin, N., & Pokrajac, D. D. (2017) · Archives of Acoustics · doi:10.1515/aoa-2017-0025 · Cited by 13
“The Mel scale 1 was introduced by ( Stevens et al. , 1937) as a scale of pitches which are subjectively equal in distance from one another.”
MethodCadore, J., Valverde-Albacete, F. J., Gallardo-Antolín, A., et al. (2012) · Cognitive Computation · doi:10.1007/s12559-012-9196-6 · Cited by 13
“The Mel scale is a very well-known logarithmic transformation of the frequency scale due to Stevens, Volkman and Newmann in 1937 [40]:”
BackgroundA Comparative Study of Phoneme Recognition using GMM-HMM and ANN based Acoustic Modeling
Fauziya, F., & Nijhawan, G. (2014) · International Journal of Computer Applications · doi:10.5120/17186-7366 · Cited by 13
Vowel Change in New Zealand English - Patterns and Implications
Langstrof, C. (2006) · UC Research Repository (University of Canterbury) · doi:10.26021/3782 · Cited by 13
Analysis of affective expression in speech
Shikler, T. S. (2021) · CL Technical Reports · doi:10.48456/tr-740 · Cited by 14
Transfer Learning in Heart Sound Classification using Mel spectrogram
Li, X., Schlindwein, F. S., & Ng, G. A. (2022) · Computing in cardiology · doi:10.22489/cinc.2022.046 · Cited by 12
“signal across time on a scale that is more suitable to humans, as we perceive frequency logarithmically [5].”
BackgroundLee, J. K., Kim, B. K., Choi, H., et al. (2024) · Applied Acoustics · doi:10.1016/j.apacoust.2024.110194 · Cited by 12
SATEER: Subject-Aware Transformer for EEG-Based Emotion Recognition
Lanzino, R., Avola, D., Fontana, F., Cinque, L., Scarcello, F., & Foresti, G. L. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065725500029 · Cited by 12
Transmutative voice conversion
Mohammadi, S. H., & Kain, A. B. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639003 · Cited by 12
“Then, for each frame, we resample the source and target magnitude spectra using 100 points on the Mel scale [20], using spline interpolation.”
MethodAnalysis of speech MEL scale and its classification as big data by parameterized KNN
Skuratovskii, R., Bazarna, A., & Osadhyy, E. (2021) · Artificial Intelligence · doi:10.15407/jai2021.01.042 · Cited by 12
“Mel-scale are the most popular variant used today, even if there is no theoretical reason that the Mel-scale is superior to the other scales [58].”
BackgroundCOVID-19 Diagnosis from Cough Acoustics using ConvNets and Data Augmentation
Mahanta, S. K., Kaushik, D., Truong, H. V., Jain, S., & Guha, K. (2021) · International Conference on Advances in Computing and Future Communication Technologies (ICACFCT) · doi:10.1109/icacfct53978.2021.9837350 · Cited by 12
Venomave: Targeted Poisoning Against Speech Recognition
Aghakhani, H., Schönherr, L., Eisenhofer, T., Kolossa, D., Holz, T., Kruegel, C., & Vigna, G. (2023) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml54575.2023.00035 · Cited by 16
PS3DT: Synthetic Speech Detection Using Patched Spectrogram Transformer
Yadav, A. K. S., Xiang, Z., Bhagtani, K., Bestagini, P., Tubaro, S., & Delp, E. J. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00075 · Cited by 12
“The conversion between Hertz frequency scale f Hz and the mel frequency scale f mel [14] is obtained by Recently, transformer networks have been used with spec-trograms [14], [15] for general audio classification tasks [26]– [28].”
BackgroundMethodInfluentialPurging Musical Instrument Sample Databases Using Automatic Musical Instrument Recognition Methods
Livshin, A., & Xavier Rodet, A. (2009) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2009.2018439 · Cited by 12
“Features computed using a model of the human hearing process (see [11] for the Mel scale and [12] for the Bark scale): Mel Frequency Cepstral Coefficients (MFCCs), Delta MFCC, Delta-Delta MFCC, Loudness, Relative Specific Loudness, Fluctuation Length, Mean Fluctuation Length, Roughness, Sharpness,…”
MethodAssociation Rule Mining and Audio Signal Processing for Music Discovery and Recommendation
Siddiquee, M. M. R., Rahman, M. S., Chowdhury, S. U. I., et al. (2016) · International Journal of Software Innovation · doi:10.4018/ijsi.2016040105 · Cited by 12
Perceived Relatedness of Musical Tones in Major and Minor Tonal Contexts
Harris, R. W. (1985) · The American Journal of Psychology · doi:10.2307/1422513 · Cited by 12
FairSSD: Understanding Bias in Synthetic Speech Detectors
Singh Yadav, A. K., Bhagtani, K., Salvi, D., Bestagini, P., & Delp, E. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00445 · Cited by 12
“The frequency scale can be linear [46], logarithmic [18,44] or it can be based on mel-filters [47,48].”
BackgroundCue-shifting between acoustic cues: Evidence for directional asymmetry
Yang, M., & Sundara, M. (2019) · Journal of Phonetics · doi:10.1016/j.wocn.2019.04.002 · Cited by 10
“The decision to use the acoustic scale also seemed appropriate given that the relationship between Hertz and mels is linear below 500 Hz (Stevens et al., 1937).”
MethodAn ensemble-based framework for mispronunciation detection of Arabic phonemes
Çalık, Ş. S., Küçükmanіşa, A., & Kilimci, Z. H. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2023.109593 · Cited by 11
SEMOUR: A Scripted Emotional Speech Repository for Urdu
Zaheer, N., Ahmad, O. U., Ahmed, A., Khan, M. S., & Shabbir, M. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445171 · Cited by 11
“They vary from using Mel-frequency Cepstral Coefficients (MFCC) [43] to using Mel-frequency spectrogram [48].”
MethodLearning Temporal Resolution in Spectrogram for Audio Classification
Liu, H., Liu, X., Kong, Q., Wang, W., & Plumbley, M. D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i12.29294 · Cited by 15
“There are plenty of studies on learning a suitable frequency resolution with a similar spirit (Stevens, Volkmann, and Newman 1937; Sainath et al. 2013; Ravanelli and Bengio 2018b; Zeghidour et al. 2021).”
BackgroundAccelerated construction of stress relief music datasets using CNN and the Mel-scaled spectrogram
Choi, S., Park, J.-I., Hong, C.-H., Park, S.-G., & Park, S.-C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300607 · Cited by 11
Rakowski, A. Z. (2009) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 11
Emotion Recognition Using Vocal Tract Information
Krothapalli, S. R., & Koolagudi, S. G. (2012) · Springer briefs in electrical and computer engineering · doi:10.1007/978-1-4614-5143-3_4 · Cited by 11
Music Recommendation: Audio Neighbourhoods to Discover Music in the Long Tail
Craw, S., Horsburgh, B., & Massie, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24586-7_6 · Cited by 12
“Next, each window is discretised into a feature vector, based on the mel-scale [20].”
MethodLearning spectro-temporal representations of complex sounds with parameterized neural networks
Riad, R., Karadayi, J., Bachoud‐Lévi, A., & Emmanuel Dupoux, A. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0005482 · Cited by 11
“Working from a spectral representation has biological plausibility, since it matches approximately what we know about the first stage of auditory processing (Stevens et al., 1937).”
BackgroundChen, H., Liu, Z., Liu, Z., & Zhang, P. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0005202 · Cited by 12
“However, most of the conventional features, such as the Mel-scale filter bank coefficients (FBank) [7], are short-term and designed for tasks such as speech recognition.”
BackgroundGenerative adversarial learning for improved data efficiency in underwater target classification
Chandran, C. S., Kamal, S., Mujeeb, A., et al. (2021) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2021.07.006 · Cited by 11
Computational Models of Similarity for Drum Samples
Pampalk, E., Herrera, P., & Goto, M. (2008) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2007.910783 · Cited by 11
“The questions we address are: 1) How does the MPEG-7 model compare to the auditory image based model?”
BackgroundIndividual pitch functions and pitch-duration cross-dimensional matching
Painton, S. W., Cullinan, W. L., & Mencke, E. O. (1977) · Perception & Psychophysics · doi:10.3758/bf03199504 · Cited by 11
Zwicker, E. (1975) · Handbook of sensory physiology · doi:10.1007/978-3-642-65995-9_9 · Cited by 11
Osiński, D., Łukowska, M., Hjelme, D. R., & Wierzchoń, M. (2021) · Sensors · doi:10.3390/s21217351 · Cited by 11
“Be pleasant for the user [25]; in amplitude corresponding to the maximal color intensity to avoid masking chosen cross-modal correspondences yellow—middle-high pitch,”
BackgroundMethodRobust speech command recognition in challenging industrial environments
Bini, S., Carletti, V., Saggese, A., & Vento, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.107938 · Cited by 9
Female African elephant rumbles differ between populations and sympatric social groups
Pardo, M. A., Lolchuragi, D. S., Poole, J. H., Granli, P., Moss, C. J., Douglas‐Hamilton, I., & Wittemyer, G. (2024) · Royal Society Open Science · doi:10.1098/rsos.241264 · Cited by 10
“A mel spectrogram is similar to a traditional spectrogram (raster plot with time on the x -axis, frequency on the y -axis, and amplitude indicated by pixel darkness) but with frequency transformed to the logarithmic mel scale [52].”
MethodA Dynamic Voiceprint Fusion Mechanism With Multispectrum for Noncontact Bearing Fault Diagnosis
Liu, Z., Chen, Y., Zhang, D., & Guo, F. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3530972 · Cited by 13
“The Mel spectrum mechanism can be expressed as follows [23]: where f stands for the Hertz frequency and Mel ( f ) represents the corresponding Mel frequency value.”
Attention-based multi-modal learning for aircraft engine fan fault diagnosis
Zhu, J., Liang, S., Ma, Z., et al. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.110194 · Cited by 10
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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