Active noise cancellation using hybrid CNN-LSTM classification and genetic algorithm-based community detection extraction with LMS noise filtering process

Unwanted signals in information-bearing signal referred to as noise could degrade the strength of signals in terms of intelligibility and quality. Over the decade, various researchers developed algorithms to enhance speech signal quality and noise reduction. To address the issue, the study propounded the active noise cancellation method by using a hybridized convolutional neural network–long short-term memory (CNN-LSTM) approach and genetic algorithm (GA)-based community detection feature extraction enhanced with least mean square (LMS) noise filtering process. The quality filtered signals were extracted with feature correlation data and precise relevant features using genetic algorithm-based community detection. The selective parameters aid the classification performance, facilitated by hyperparameter fine-tuning of GA-based community detection. The results of incorporating LSTM will eliminate unnecessary memory content by correlating past information outcomes to classify new feature values. In this method, abnormal and normal signals are classified by our LSTM layers output. These classified outcomes aid in the denoise of active voice signals with a fast convergence rate. The efficiency of the proposed method was assessed in terms of its performance through comparative active noise cancellation (ANC) methods. The higher accuracy rate and low NMSE in the classification of audio signals evidenced the efficacy of the proposed ANC model.