An improved method of audio denoising based on wavelet transform. Impulse noise reduction in audio signal through multi-stage technique. In Proceedings of the IEEE international conference on engineering and technology. Understanding of a convolutional neural network. From the cosine similarity, it has been proved that MLCNN provides high security level which can be used for many secure applications.Īlbawi, S., Mohammed, T. Form the comparisons it has been observed that the proposed MLCNN model outperforms other models. The proposed MLCNN model has been compared with the reported models. The performance of MLCNN has been evaluated using short-time objective intelligibility, perceptual evaluation of speech quality and Cosine similarities. From the validation it has been found that the proposed MLCNN model provides an accuracy of 93.25%. The proposed method has been verified and validated MNIST database. The proposed MLCNN models has been trained and tested as 80:20 and 70:30 ratios from the available database. The proposed MLCNN takes the input as MFCC with different frames from the noise contaminated audio signal for training and testing. In this research article, a multi-layered convolutional neural network (MLCNN) based auto-CODEC for audio signal enhancement which is utilizing the Mel-frequency cepstral coefficients (MFCC) has been proposed.
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