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A harmonic-based musical scaling method with natural number frequencies

Year 2025, Volume: 13 Issue: 1, 19 - 37, 30.03.2025
https://doi.org/10.12975/rastmd.20251312

Abstract

General acceptance arises from the most convincing method among the available options. Similarly, while the Western chromatic scale is the most widely used system today, it has limitations in representing harmonious intervals, microtonal performances, and the weak resonant effects of fractional frequencies This study introduces the Safir method, a novel approach to redefining musical note frequencies within an octave interval. Unlike traditional scales, Safir employs natural number-based values, ensuring more harmonious intervals and enhanced tuning consistency. A key strength of Safir lies in its ability to overcome the limitations of conventional tuning systems. The Safir method enhances spectral coherence by aligning note frequencies with the harmonic distribution of the Fourier series and strengthening the resonance effect through natural frequencies. This method has significant potential for various applications including music, speech and signal processing, spectral leakage reduction, and healthcare. Four key advantages of the Safir scale system are its its alignment with the harmonic series, , the strong resonant effect of note frequencies derived from natural numbers, the suppression of dissonant intervals in higher frequencies across the octave band, and its linear spacing within the octave, which ensures minimal deviation from compatible intervals even in microtonal divisions. This novel method represents a major advancement in tuning and musical scales. By providing a more precise, harmonious, and resonant frequency system, Safir addresses key shortcomings of traditional musical scales and opens new possibilities in both theoretical and practical domains.

References

  • Aktas, M. E., Akbas, E., Papayik, J., & Kovankaya, Y. (2019). Classification of Turkish makam music: A topological approach. Journal of Mathematics and Music, 13(2), 135-149. https://doi.org/10.1080/17459737 .2019.1622810
  • Altun, F., & Egermann, H. (2021). Temperament systems influence emotion induction but not makam recognition performance in Turkish makam music. Psychology of Music, 49(5), 1088-1101. https://doi.org/10.1177/0305735620922892
  • Álvarez, A., Martínez, R., Gómez, P., Nieto, V., & Rodellar, V. (2007). A robust mel-scale subband voice activity detector for a car platform. Interspeech 2007, pp.226-229. https://doi.org/10.21437/ Interspeech.2007-92
  • Ashton-Bell, R. L. T. (2019). On the Geometric Realisation of Equal Tempered Music. Mapana Journal of Sciences, 18(3), 53-75. https:// doi.org/10.12723/mjs.50.5
  • Bailes, F., Dean, R. T., & Broughton, M. C. (2015). How Different Are Our Perceptions of Equal-Tempered and Microtonal Intervals? A Behavioural and EEG Survey. PLOS ONE, 10(8), Article no:e0135082. https://doi. org/10.1371/journal.pone.0135082
  • Bozkurt, B., Ayangil, R., & Holzapfel, A. (2014). Computational Analysis of Turkish Makam Music: Review of State-of-the-Art and Challenges. Journal of New Music Research, 43(1), 3-23. https://doi.org/10.1080/09298 215.2013.865760
  • Brown, J. L. (2016). The Psychological Representation of Musical Intervals in a Twelve-Tone Context. Music Perception, 33(3), 274-286. https://doi.org/10.1525/ mp.2016.33.3.274
  • Clader, E. (2018). Why Twelve Tones? The Mathematics of Musical Tuning. The Mathematical Intelligencer, 40(3), 32-36. https://doi.org/10.1007/s00283-017-9759-1
  • Crismani, D. J. (2022). The Microtonal String Orchestra. Perspectives of New Music, 60(1), 113-151. https://doi.org/10.1353/ pnm.2022.a902893
  • Guers, M. J. (2023). Consistent physical frequencies in time-frequency analysis. The Journal of the Acoustical Society of America, 154(4_supplement), A209-A209. https://doi. org/10.1121/10.0023302
  • Hinrichsen, H. (2016). Revising the musical equal temperament. Revista Brasileira de Ensino de Física, 38(1), 1310-1313. https:// doi.org/10.1590/S1806-11173812105
  • Ijaz, N., Banoori, F., & Koo, I. (2024). Reshaping Bioacoustics Event Detection: Leveraging Few-Shot Learning (FSL) with Transductive Inference and Data Augmentation. Bioengineering, 11(7), 685-704. https://doi.org/10.3390/ bioengineering11070685
  • Isaacson, E. (2023). Visualizing Music. Indiana University Press. https://doi.org/10.2307/ jj.2131181
  • Kellermann, W., Martin, R., & Ono, N. (2023). Signal processing and machine learning for speech and audio in acoustic sensor networks. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 54, s13636-023-00322-00326. https://doi. org/10.1186/s13636-023-00322-6
  • Konar, S. (2019a). The Sounds of Music: Science of Musical Scales: 1. Human Perception of Sound. Resonance, 24(8), 891- 900. https://doi.org/10.1007/s12045-019- 0851-z
  • Konar, S. (2019b). The Sounds of Music: Science of Musical Scales: II: Western Classical Music. Resonance, 24(9), 1015- 1023. https://doi.org/10.1007/s12045-019- 0867-4
  • Lindley, M. (2001). Just intonation (C.1). Oxford University Press. https:// doi.org/10.1093/gmo/9781561592630. article.1
  • Liu, G., Cai, S., & Wang, C. (2023). Speech emotion recognition based on emotion perception. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), Article no:22. https://doi.org/10.1186/ s13636-023-00289-4
  • Moore, B. C. J., Tyler, L. K., & Marslen-Wilson, W. (2008). Introduction. The perception of speech: From sound to meaning. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1493), 917-921. https://doi.org/10.1098/ rstb.2007.2195
  • Parncutt, R. (2024). Psychoacoustic Foundations of Major-Minor Tonality. The MIT Press. https://doi.org/10.7551/ mitpress/15050.001.0001
  • Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., Burriel-Valencia, J., Pineda-Sanchez, M., Perez-Cruz, J., & Riera- Guasp, M. (2021). New Method for Spectral Leakage Reduction in the FFT of Stator Currents: Application to the Diagnosis of Bar Breakages in Cage Motors Working at Very Low Slip. IEEE Transactions on Instrumentation and Measurement, 70, 1-11. https://doi. org/10.1109/TIM.2021.3056741
  • Schwartz, D. A., Howe, C. Q., & Purves, D. (2003). The Statistical Structure of Human Speech Sounds Predicts Musical Universals. The Journal of Neuroscience, 23(18), 7160-7168. https://doi.org/10.1523/ JNEUROSCI.23-18-07160.2003
  • Sereda, M., Xia, J., El Refaie, A., Hall, D. A., & Hoare, D. J. (2018). Sound therapy (using amplification devices and/or sound generators) for tinnitus. Cochrane Database of Systematic Reviews, 12(12). https://doi. org/10.1002/14651858.CD013094.pub2
  • Smit, E. A., Milne, A. J., Dean, R. T., & Weidemann, G. (2019), Perception of affect in unfamiliar musical chords. PLOS ONE, 14(6), Article no: e0218570. https://doi. org/10.1371/journal.pone.0218570
  • Thoegersen, P. (2024). Maqam Melodies: Pitches, Patterns, and Developments of Music in the Middle East and other Microtonal Writings (1st Ed). Jenny Stanford Publishing. https://doi.org/10.1201/9781003492856
  • Uyar, B., Atli, H. S., Şentürk, S., Bozkurt, B., & Serra, X. (2014). A Corpus for Computational ResearchofTurkishMakamMusic.Proceedings of the 1st International Workshop on Digital Libraries for Musicology, 1-7. https://doi. org/10.1145/2660168.2660174
  • Wang, Y., Chen, K., Gou, X., He, R., Zhou, W., Yang, S., & Qiu, G. (2021). A High- Precision and Wideband Fundamental Frequency Measurement Method for Synchronous Sampling Used in the Power Analyzer. Journal on Frontiers in Energy Research, 9, Article no: 652386. https://doi. org/10.3389/fenrg.2021.652386
  • Yost, W. A. (2009). Pitch perception. Attention, Perception & Psychophysics, 71(8), 1701-1715. https://doi.org/10.3758/ APP.71.8.1701

A harmonic-based musical scaling method with natural number frequencies

Year 2025, Volume: 13 Issue: 1, 19 - 37, 30.03.2025
https://doi.org/10.12975/rastmd.20251312

Abstract

General acceptance arises from the most convincing method among the available options. Similarly, while the Western chromatic scale is the most widely used system today, it has limitations in representing harmonious intervals, microtonal performances, and the weak resonant effects of fractional frequencies This study introduces the Safir method, a novel approach to redefining musical note frequencies within an octave interval. Unlike traditional scales, Safir employs natural number-based values, ensuring more harmonious intervals and enhanced tuning consistency. A key strength of Safir lies in its ability to overcome the limitations of conventional tuning systems. The Safir method enhances spectral coherence by aligning note frequencies with the harmonic distribution of the Fourier series and strengthening the resonance effect through natural frequencies. This method has significant potential for various applications including music, speech and signal processing, spectral leakage reduction, and healthcare. Four key advantages of the Safir scale system are its its alignment with the harmonic series, , the strong resonant effect of note frequencies derived from natural numbers, the suppression of dissonant intervals in higher frequencies across the octave band, and its linear spacing within the octave, which ensures minimal deviation from compatible intervals even in microtonal divisions. This novel method represents a major advancement in tuning and musical scales. By providing a more precise, harmonious, and resonant frequency system, Safir addresses key shortcomings of traditional musical scales and opens new possibilities in both theoretical and practical domains.

Ethical Statement

This research does not require ethics committee approval. The author declares that the method presented in this study is subject to a patent application under the author’s name, which may have potential commercial implications. The datasets used and analyzed in the current study are publicly available as open- source data.

Supporting Institution

No financial support was used in this research.

References

  • Aktas, M. E., Akbas, E., Papayik, J., & Kovankaya, Y. (2019). Classification of Turkish makam music: A topological approach. Journal of Mathematics and Music, 13(2), 135-149. https://doi.org/10.1080/17459737 .2019.1622810
  • Altun, F., & Egermann, H. (2021). Temperament systems influence emotion induction but not makam recognition performance in Turkish makam music. Psychology of Music, 49(5), 1088-1101. https://doi.org/10.1177/0305735620922892
  • Álvarez, A., Martínez, R., Gómez, P., Nieto, V., & Rodellar, V. (2007). A robust mel-scale subband voice activity detector for a car platform. Interspeech 2007, pp.226-229. https://doi.org/10.21437/ Interspeech.2007-92
  • Ashton-Bell, R. L. T. (2019). On the Geometric Realisation of Equal Tempered Music. Mapana Journal of Sciences, 18(3), 53-75. https:// doi.org/10.12723/mjs.50.5
  • Bailes, F., Dean, R. T., & Broughton, M. C. (2015). How Different Are Our Perceptions of Equal-Tempered and Microtonal Intervals? A Behavioural and EEG Survey. PLOS ONE, 10(8), Article no:e0135082. https://doi. org/10.1371/journal.pone.0135082
  • Bozkurt, B., Ayangil, R., & Holzapfel, A. (2014). Computational Analysis of Turkish Makam Music: Review of State-of-the-Art and Challenges. Journal of New Music Research, 43(1), 3-23. https://doi.org/10.1080/09298 215.2013.865760
  • Brown, J. L. (2016). The Psychological Representation of Musical Intervals in a Twelve-Tone Context. Music Perception, 33(3), 274-286. https://doi.org/10.1525/ mp.2016.33.3.274
  • Clader, E. (2018). Why Twelve Tones? The Mathematics of Musical Tuning. The Mathematical Intelligencer, 40(3), 32-36. https://doi.org/10.1007/s00283-017-9759-1
  • Crismani, D. J. (2022). The Microtonal String Orchestra. Perspectives of New Music, 60(1), 113-151. https://doi.org/10.1353/ pnm.2022.a902893
  • Guers, M. J. (2023). Consistent physical frequencies in time-frequency analysis. The Journal of the Acoustical Society of America, 154(4_supplement), A209-A209. https://doi. org/10.1121/10.0023302
  • Hinrichsen, H. (2016). Revising the musical equal temperament. Revista Brasileira de Ensino de Física, 38(1), 1310-1313. https:// doi.org/10.1590/S1806-11173812105
  • Ijaz, N., Banoori, F., & Koo, I. (2024). Reshaping Bioacoustics Event Detection: Leveraging Few-Shot Learning (FSL) with Transductive Inference and Data Augmentation. Bioengineering, 11(7), 685-704. https://doi.org/10.3390/ bioengineering11070685
  • Isaacson, E. (2023). Visualizing Music. Indiana University Press. https://doi.org/10.2307/ jj.2131181
  • Kellermann, W., Martin, R., & Ono, N. (2023). Signal processing and machine learning for speech and audio in acoustic sensor networks. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 54, s13636-023-00322-00326. https://doi. org/10.1186/s13636-023-00322-6
  • Konar, S. (2019a). The Sounds of Music: Science of Musical Scales: 1. Human Perception of Sound. Resonance, 24(8), 891- 900. https://doi.org/10.1007/s12045-019- 0851-z
  • Konar, S. (2019b). The Sounds of Music: Science of Musical Scales: II: Western Classical Music. Resonance, 24(9), 1015- 1023. https://doi.org/10.1007/s12045-019- 0867-4
  • Lindley, M. (2001). Just intonation (C.1). Oxford University Press. https:// doi.org/10.1093/gmo/9781561592630. article.1
  • Liu, G., Cai, S., & Wang, C. (2023). Speech emotion recognition based on emotion perception. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), Article no:22. https://doi.org/10.1186/ s13636-023-00289-4
  • Moore, B. C. J., Tyler, L. K., & Marslen-Wilson, W. (2008). Introduction. The perception of speech: From sound to meaning. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1493), 917-921. https://doi.org/10.1098/ rstb.2007.2195
  • Parncutt, R. (2024). Psychoacoustic Foundations of Major-Minor Tonality. The MIT Press. https://doi.org/10.7551/ mitpress/15050.001.0001
  • Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., Burriel-Valencia, J., Pineda-Sanchez, M., Perez-Cruz, J., & Riera- Guasp, M. (2021). New Method for Spectral Leakage Reduction in the FFT of Stator Currents: Application to the Diagnosis of Bar Breakages in Cage Motors Working at Very Low Slip. IEEE Transactions on Instrumentation and Measurement, 70, 1-11. https://doi. org/10.1109/TIM.2021.3056741
  • Schwartz, D. A., Howe, C. Q., & Purves, D. (2003). The Statistical Structure of Human Speech Sounds Predicts Musical Universals. The Journal of Neuroscience, 23(18), 7160-7168. https://doi.org/10.1523/ JNEUROSCI.23-18-07160.2003
  • Sereda, M., Xia, J., El Refaie, A., Hall, D. A., & Hoare, D. J. (2018). Sound therapy (using amplification devices and/or sound generators) for tinnitus. Cochrane Database of Systematic Reviews, 12(12). https://doi. org/10.1002/14651858.CD013094.pub2
  • Smit, E. A., Milne, A. J., Dean, R. T., & Weidemann, G. (2019), Perception of affect in unfamiliar musical chords. PLOS ONE, 14(6), Article no: e0218570. https://doi. org/10.1371/journal.pone.0218570
  • Thoegersen, P. (2024). Maqam Melodies: Pitches, Patterns, and Developments of Music in the Middle East and other Microtonal Writings (1st Ed). Jenny Stanford Publishing. https://doi.org/10.1201/9781003492856
  • Uyar, B., Atli, H. S., Şentürk, S., Bozkurt, B., & Serra, X. (2014). A Corpus for Computational ResearchofTurkishMakamMusic.Proceedings of the 1st International Workshop on Digital Libraries for Musicology, 1-7. https://doi. org/10.1145/2660168.2660174
  • Wang, Y., Chen, K., Gou, X., He, R., Zhou, W., Yang, S., & Qiu, G. (2021). A High- Precision and Wideband Fundamental Frequency Measurement Method for Synchronous Sampling Used in the Power Analyzer. Journal on Frontiers in Energy Research, 9, Article no: 652386. https://doi. org/10.3389/fenrg.2021.652386
  • Yost, W. A. (2009). Pitch perception. Attention, Perception & Psychophysics, 71(8), 1701-1715. https://doi.org/10.3758/ APP.71.8.1701
There are 28 citations in total.

Details

Primary Language English
Subjects Music Technology and Recording, Theories of Music, Musicology and Ethnomusicology
Journal Section Original research
Authors

Selma Ozaydin 0000-0002-4613-9441

Early Pub Date March 26, 2025
Publication Date March 30, 2025
Submission Date December 20, 2024
Acceptance Date March 4, 2025
Published in Issue Year 2025 Volume: 13 Issue: 1

Cite

APA Ozaydin, S. (2025). A harmonic-based musical scaling method with natural number frequencies. Rast Musicology Journal, 13(1), 19-37. https://doi.org/10.12975/rastmd.20251312

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