Optimization Parameter of the 1P Keys Interpolation Kernel Implemented in the Correlation Algorithm for Estimating the Fundamental Frequency of the Speech Signal
Abstract
The first part of this paper describes an algorithm for estimating the fundamental frequency F0 of a speech signal, using an autocorrelation algorithm. After that, it was shown that, due to the discrete structure of the autocorrelation function, the accuracy of the fundamental frequency estimate largely depends on the sampling period TS. Then, in order to increase the accuracy of the estimation, an interpolation of the correlation function is performed. Interpolation is performed using a one parameter (1P) Keys interpolation kernel. The second part of the paper presents an experiment in which the optimization of the 1P Keys kernel parameter was performed. The experiment was performed on test Sine and Speech signals, in the conditions of ambient disturbances (N8 Babble noise, SNR = 5 to -10 dB). MSE was used as a measure of the accuracy of the fundamental frequency estimate. Kernel parameter optimization was performed on the basis of the MSE minimum. The results are presented graphically and tabularly. Finally, a comparative analysis of the results was performed. Based on the comparative analysis, the window function, in which the smallest estimation error was achieved for all ambient noise conditions, was chosen.
References
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Abstract
References
- De Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930.
- Kacha, A., Grenez, F., & Benmahammed, K. (2005). Time–frequency analysis and instantaneous frequency estimation using two-sided linear prediction. Signal Processing, 85(3), 491-503.
- Keys, R. (1981). Cubic convolution interpolation for digital image processing. IEEE transactions on acoustics, speech, and signal processing, 29(6), 1153-1160.
- Meijering, E., & Unser, M. (2003). A note on cubic convolution interpolation. IEEE Transactions on Image processing, 12(4), 477-479.
- Milivojević, Z. N., & Balanesković, D. Z. (2009). Enhancement of the perceptive quality of the noisy speech signal by using of DFF-FBC algorithm. Facta universitatis-series: Electronics and Energetics, 22(3), 391-404.
- Milivojević, Z. N., & Brodić, D. (2013). Estimation of the fundamental frequency of the speech signal compressed by mp3 algorithm. Archives of Acoustics, 363-373.
- Milivojevic, Z., & Prlincevic, B. (2021). Estimation of the fundamental frequency of the speech signal using autocorrelation algorithm. Unitech.
- Milivojevic, Z. N., Brodic, D. T., & Stojanovic, V. (2017). Estimation of fundamental frequency with autocorrelation algorithm. http://vtsnis.edu.rs/wp-
- Pang, H. S., Baek, S., & Sung, K. M. (2000). Improved fundamental frequency estimation using parametric cubic convolution. IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences, 83(12), 2747-2750.
- Qiu, L., Yang, H., & Koh, S.-N. (1995). Fundamental frequency determination based on instantaneous frequency estimation. Signal Processing, 44(2), 233-241.
- Rabiner, L.R. and Schafer, R.W. (1978) Digital processing of speech signals. Prentice Hall.
- Rao, P., & Barman, A. D. (2000). Speech formant frequency estimation: evaluating a nonstationary analysis method. Signal Processing, 80(8), 1655-1667.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 31, 2021 |
| Published in Issue | Year 2021 |
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