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A Harmonic-Based Musical Scaling Method With Natural Number Frequencies
(Genc Bilge Yayincilik, 2025) Ozaydin, S.
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. © 2025, Genc Bilge Yayincilik. All rights reserved.
Citation - Scopus: 2
Design of a Voice Activity Detection Algorithm Based on Logarithmic Signal Energy
(Institute of Electrical and Electronics Engineers Inc., 2022) Ozaydin, S.
This article presents a new method for calculating the signal energies of speech segments in voice activity detection algorithms. In the study, the μ-law signal compression method is adapted to calculate short-term signal energies. A simple voice activity detection (VAD) algorithm is designed to demonstrate the effectiveness of the proposed method. The same VAD algorithm was also run with two different conventional energy calculation formulas and the performance of each VAD was evaluated using time-domain short-time energy features. The G729 standard VAD algorithm was also used for performance comparison. During the test of the analyzed detectors, many kinds of input speech signals with various types of background environmental noise, such as restaurants, vehicles, and streets, were tested. Using the new energy calculation method, the VAD detector has improved detection accuracy compared to VAD detectors based on the other two energy methods and was able to effectively identify voice-active regions even in noisy conditions at low SNR levels. The results revealed that the VAD detector designed with the proposed new energy calculation formula outperforms traditional energy-based voice activity detection methods and provides noticeable increases in detection rate even under adverse conditions. © 2022 IEEE.
A Graphical Speech Analysis Teaching Tool
(Institute of Electrical and Electronics Engineers Inc., 2022) Ozaydin, S.
The widespread use of digital speech processing in today's technologies causes many electronics and computer engineering students to need a basic background in these subjects. The paper describes a toolbox designed to support undergraduate or graduate level courses on speech processing. The proposed educational toolbox is designed as a virtual lab for basic operations in digital speech processing-based courses. This graphical user interface (GUI) based speech analysis algorithm is built with six main function modules, which are signal input, noise addition, up-sampling/down-sampling, time domain feature analysis, pitch detection and frequency domain analysis. The toolbox involves different operations for measuring important speech feature parameters such as pitch, energy, zero-crossing ratio, FFT and power spectrum of an input speech signal. The toolbox has also been developed to easily manipulate and add some other possible speech processing methods. It is thought that the tool will make it easier for students to understand the methods that form the basis of digital speech processing, increase the interest in the lesson with its visual outputs, and allow new methods to be added easily when desired thanks to its simple and modular structure. The main aim of this paper to show how such a tool facilitates students understanding of technical concepts introduced in speech courses. © 2022 IEEE.

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