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Hybridization of spectral filtering with particle swarm optimization for speech signal enhancement

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Abstract

Speech enhancement has received a significant amount of research attention over the past several decades. The enhancement of speech signal is needed so as to improve the degraded signal and the goal is to separate a single mixture into its underlying clean speech and interferer components. This is achieved by having prior knowledge through learning and generation of masks accordingly. Hybridization of the spectral filtering and optimization algorithm is employed for speech enhancement in this paper. The proposed technique uses MMSE (Minimum Mean Squared Error) and PSO (Particle Swarm Optimization) for effective enhancement. The proposed technique is three module technique consisting of pre-processing module, optimization module and spectral filtering module. Loizou’s database and Aurora dataset are used for evaluating the proposed technique using standard evaluation metrics consists of PESQ and SNR. Comparative analysis is also made by comparing with other existing techniques such as MMSE and BNMF. Highest PESQ for proposed technique is 2.75 and highest SNR came about 32.97. The technique gave average PESQ of 2.18 and average SNR of 20.53 which was higher than the average values for other techniques. Hence, we can observe that proposed technique yielded better evaluation metrics than the existing methods.

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Authors and Affiliations

  1. Electronics and Communication Engineering, Easwari Engineering College, Ramapuram, Anna University, Chennai, India

    R. Senthamizh Selvi

  2. Department of Electronics and Communication Engineering, Easwari Engineering College, Ramapuram, Anna University, Chennai, India

    G. R. Suresh

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  1. R. Senthamizh Selvi
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  2. G. R. Suresh
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Correspondence to R. Senthamizh Selvi.

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Selvi, R.S., Suresh, G.R. Hybridization of spectral filtering with particle swarm optimization for speech signal enhancement. Int J Speech Technol 19, 19–31 (2016). https://doi.org/10.1007/s10772-015-9317-1

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  • DOI: https://doi.org/10.1007/s10772-015-9317-1

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