scholarly journals Signal periodicity detection using Ramanujan subspace projection

2020 ◽  
Vol 71 (5) ◽  
pp. 326-332
Author(s):  
Deepa Abraham ◽  
Manju Manuel
Keyword(s):  
Signal Processing ◽  
Detection Methods ◽  
Subspace Projection ◽  
Speech Signal Processing ◽  
Periodicity Detection ◽  
Periodic Decomposition ◽  
Signal Period ◽  
Ramanujan Sums ◽  
Pitch Analysis ◽  
Detection Of Signals

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.

scholarly journals In Memoriam: Johan Liljencrants (1936–2012)

2012 ◽  
Vol 42 (2) ◽  
pp. 253-254
Author(s):  
Rolf Carlson ◽  
Björn Granström
Keyword(s):  
Signal Processing ◽  
Speech Signal ◽  
Speech Analysis ◽  
Speech Signal Processing ◽  
Speech Synthesizer ◽  
Speech Transmission ◽  
Analysis And Synthesis ◽  
In Memoriam ◽  
The 1960S

Johan Liljencrants was a KTH oldtimer. His interests focused early on speech analysis and synthesis where in the 1960s he took a leading part in the development of analysis hardware, the OVE III speech synthesizer, and the introduction of computers in the Speech Transmission Laboratory. Later work shifted toward general speech signal processing, for instance in his thesis on the use of a reflection line synthesizer. His interests expanded to modelling the glottal system, parametrically as in the Liljencrants–Fant (LF) model of glottal waveshapes, as well as physically including glottal aerodynamics and mechanics.

The construction of wavelet network for speech signal processing

2005 ◽  
Vol 15 (3-4) ◽  
pp. 217-222 ◽  
Author(s):  
D. Shi ◽  
F. Chen ◽  
G. S. Ng ◽  
J. Gao
Keyword(s):  
Signal Processing ◽  
Speech Signal ◽  
Wavelet Network ◽  
Speech Signal Processing

scholarly journals Periodicity Detection Method for Small-Sample Time Series Datasets

2010 ◽  
Vol 4 ◽  
pp. BBI.S5983 ◽  
Author(s):  
Daisuke Tominaga
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Small Sample ◽  
Detection Methods ◽  
Series Data ◽  
Computational Time ◽  
Signal Pathways ◽  
Periodicity Detection ◽  
Sampling Points ◽  
Sample Time

Time series of gene expression often exhibit periodic behavior under the influence of multiple signal pathways, and are represented by a model that incorporates multiple harmonics and noise. Most of these data, which are observed using DNA microarrays, consist of few sampling points in time, but most periodicity detection methods require a relatively large number of sampling points. We have previously developed a detection algorithm based on the discrete Fourier transform and Akaike's information criterion. Here we demonstrate the performance of the algorithm for small-sample time series data through a comparison with conventional and newly proposed periodicity detection methods based on a statistical analysis of the power of harmonics. We show that this method has higher sensitivity for data consisting of multiple harmonics, and is more robust against noise than other methods. Although “combinatorial explosion” occurs for large datasets, the computational time is not a problem for small-sample datasets. The MATLAB/GNU Octave script of the algorithm is available on the author's web site: http://www.cbrc.jp/%7Etominaga/piccolo/ .

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