Abstract
The quality of speech signal is more concerned in the domain of speech processing. Impulsive noise in speech signal has a capability to deteriorate the overall performance and reliability of the speech signal. This paper presents an idea of speech enhancement by amputation of impulsive disruption (noise) with the aid of modifying the dual spectrum (noisy magnitude and phase spectrum) under short time Fourier transform (STFT) domain. The processing step involves signal segmentation, short-time Fourier transforms, spectrum modification, and signal concatenation. Initially, the samples of the signal that are noisy are broke down into a limited count of frames, and each of them are assessed with coefficients generated using hamming window. The noisy signal spectrum will be attained through fast Fourier transform, and noisy components are removed using spectral subtraction. After the restoring process, the filtered signal magnitude combined with signal phase and then inverse transform followed by signal concatenation will be done to reconstruct signal. This paper mainly brings forward a proposal for extraction of features by using Mel-frequency cepstral coefficients (MFCCs). Initially, it takes the input speech signal that is enhanced in terms of reducing noise followed by the classification of emotion from the resultant output using neural network training. The simulated result shows that the proposed filtering model is more accurate.
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Srinivas, C.V.V.S., Gubbala, S., Lakshmi, N.D.N. (2022). Human Emotion Classification Based on Speech Enhancement Using Neural Networks. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_3
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DOI: https://doi.org/10.1007/978-981-19-0840-8_3
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