Abstract
Psychologists have shown that most information about the mood and attitude of a speaker is carried by the lowest (fundamental) frequency. Because of this frequency’s importance, even when the corresponding Fourier component is weak, the human brain reconstruct this frequency based on higher harmonics. The problem is that many people lack this ability. To help them better understand moods and attitudes in social interaction, it is therefore desirable to come up with devices and algorithms that would reconstruct the fundamental frequency. In this paper, we show that ideas from soft computing and computational complexity can be used for this purpose.
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Acknowledgements
This work was supported in part by the National Science Foundation grants 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and HRD-1834620 and HRD-2034030 (CAHSI Includes), and by the AT&T Fellowship in Information Technology.
It was also supported by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478, and by a grant from the Hungarian National Research, Development and Innovation Office (NRDI).
The authors are thankful to all the participants of the 27th Joint NMSU/UTEP Workshop on Mathematics, Computer Science, and Computational Sciences, Las Cruces, New Mexico, April 2, 2022 for valuable suggestions.
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Freudenthal, E., Kosheleva, O., Kreinovich, V. (2023). How to Detect the Fundamental Frequency: Approach Motivated by Soft Computing and Computational Complexity. In: Ceberio, M., Kreinovich, V. (eds) Uncertainty, Constraints, and Decision Making. Studies in Systems, Decision and Control, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-36394-8_57
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DOI: https://doi.org/10.1007/978-3-031-36394-8_57
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