Signal periodicity detection using Ramanujan subspace projection

Abstract

Signal periodic decomposition and periodic estimation are two crucial problems in the signal processing domain. Due to its significance, the applications have been extended to fields like periodic sequence analysis of biomolecules, stock market predictions, speech signal processing, and musical pitch analysis. The recently proposed Ramanujan sums (RS) based transforms are very useful in analysing the periodicity of signals. This paper proposes a method for periodicity detection of signals with multiple periods based on autocorrelation and Ramanujan subspace projection with low computational complexity. The proposed method is compared with other signal periodicity detection methods and the results show that the proposed method detects the signal period correctly in less time.

High-frequency Noise Removal of Audio Files using Daubechies Wavelet Transform

“…Several forms of noise can be found in a vintage audio recording. Major forms of such noises are random noise, pop noise and localized random noise (Abraham, 2017). These noises heavily affect the quality of original audio signals.…”