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Improving time–frequency resolution in non-stationary signal analysis using a convolutional recurrent neural network

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Abstract

In this paper, a convolutional recurrent neural network (ConvRNN) wavelet-based kernel is proposed to improve the time–frequency localization of non-stationary signals. Time–frequency distributions (TFDs) are used to localize the power spectral density components of a signal simultaneously over the time–frequency plane. For various non-stationary signals (NSS) with different features, poor time–frequency resolution is always an inherent problem in all developed TFDs. This is mainly due to inefficient segmentation methods and improper kernel adaptation. In the current work, the fraction Bezier–Bernstein polynomial function is applied to model the NSS, and points of inflection are used for signal segmentation. From the obtained segments, statistical time–frequency features are extracted and fed to a ConvRNN for better time–frequency localization. The ConvRNN employs a convolution computation between input signal features and the proposed Newton–Raphson gradient algorithm (NRGA)-based wavelet function. The optimization of the ConvRNN network is achieved by incorporating a hybrid method that combines the principles of normalized adaptive gradient descent and momentum-based optimization, with an additional normalization step to enhance convergence and stability. The ConvRNN weights are updated in both forward and backward directions (resilient propagation) until a better correlation is achieved between signal segments and the wavelet kernel. It is observed that the proposed ConvRNN NRGA wavelet improves the time–frequency localization when compared with standard TFDs and state-of-the-art methodologies. Furthermore, the proposed ConvRNN model is compared with other CNN and RNN architectures for better practical interpretations.

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The data and materials are available from the corresponding author on reasonable request.

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Acknowledgements

The author wishes to thank Research and Development centre of Sri Vasavi Engineering College for providing financial support to carry out the work.

Funding

This research is supported by Sri Vasavi Engineering college, R &D department, Government of India.

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Author notes

  1. B. Murali Krishna, S. V. V. Satyanarayana, P. V. V. Satyanarayana, and M. Venkata Suman have contributed equally to this work.

Authors and Affiliations

  1. ECE Department, Sri Vasavi Engineering College, Pedatadepalli, Tadepalligudem, A.P, 534101, India

    B. Murali Krishna, S. V. V. Satyanarayana, P. V. V. Satyanarayana & M. Venkata Suman

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  1. B. Murali Krishna
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  2. S. V. V. Satyanarayana
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  3. P. V. V. Satyanarayana
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Contributions

B and S. wrote the main manuscript text and P and M prepared Figs. 1, 2, 3, and 4. All authors reviewed the manuscript.

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Correspondence to S. V. V. Satyanarayana.

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Krishna, B.M., Satyanarayana, S.V.V., Satyanarayana, P.V.V. et al. Improving time–frequency resolution in non-stationary signal analysis using a convolutional recurrent neural network. SIViP (2024). https://doi.org/10.1007/s11760-024-03116-1

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  • DOI: https://doi.org/10.1007/s11760-024-03116-1

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