Comparison of Metaheuristic Algorithms through Application in Noisy Speech Signal using Adaptive Filtering Approach
نویسندگان
چکیده
An improved method for adaptive noise canceller (ANC) is proposed for noisy speech signal in case of random noise. In this approach, ANC is implemented through four different metaheuristic techniques. A comparative study of the performance of various metaheuristic techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS) and Modified Cuckoo Search (MCS) has been done. Fidelity parameters like signal to noise ratio (SNR) in dB, mean square error (MSE) and maximum error (ME) are observed with varying range of input SNR and it was found that the Modified Cuckoo Search based speech denoising approach give better performance in terms of SNR as compared to other Metaheuristic techniques. The quantitative (SNR, MSE and ME) and visual (Denoised speech signals) results show superiority of the proposed technique over the conventional and state -of-art speech signal denoising techniques. Keywords— Adaptive filters, Adaptive Algorithms, Artificial Bee Colony, Cuckoo search, Modified Cuckoo Search, Particle Swarm Optimization Algorithm, Speech Enhancement.
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