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Empirical mode decomposition based statistical features for discrimination of speech and low frequency music signal

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Abstract

This work aims to investigate the significance of different Empirical Mode Decomposition (EMD) based statistical features for discrimination of speech and low frequency music signal (guitar signals) which mostly lie in the frequency range of 80–1200 Hz. Each of the speech/guitar audio samples is decomposed into 10 Intrinsic Function Mode (IMFs). These IMFs are further analyzed for discriminatory evidence using statistical features like Mean, Absolute Mean, Kurtosis, Variance and Skewness. These features are then fed to different classifiers and their performances were tabulated for varying tuning parameters of the classifiers. Initial experiments were conducted on isolated features to shortlist features with best discriminatory evidence. These shortlisted features were then used in different combinations and their performances were reported. An improvement of 19.13% is observed for hybrid features over isolated features. Speech samples were obtained from Scheirer and Slaney database and Guitar samples were generated from a continuous guitar monologue uploaded on YouTube. Feature selection technique using Fisher Method and F-ratio were also implemented and best feature vectors were reported for both the algorithm. Best overall accuracy of 82.16% is reported for Hybrid features with Radial Basis Function (RBF) kernel of SVM classifier when trained with top 38 feature vectors obtained using F-Ratio Method. Different experiments verified Absolute Mean and Variance as best performing features for our task.

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Acknowledgements

We, all the authors, would like to thank Prof. Dr. Sandeep Singh Solanki, HOD and Professor, BIT Mesra, for his guidance and support in this research work. We would also like to extend my gratitude to Birla Insititute of Technology for providing us with the facilities to conduct our research.

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  1. Department of ECE, Birla Institute of Technology, Ranchi, India

    Arvind Kumar

  2. Department of ECE, Reva University, Bengaluru, India

    Mahesh Chandra

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  1. Arvind Kumar
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  2. Mahesh Chandra
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Correspondence to Arvind Kumar.

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Kumar, A., Chandra, M. Empirical mode decomposition based statistical features for discrimination of speech and low frequency music signal. Multimed Tools Appl 82, 33–58 (2023). https://doi.org/10.1007/s11042-022-13267-3

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  • DOI: https://doi.org/10.1007/s11042-022-13267-3

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