Abstract
As there is aggrandizement in the sector of artificial intelligence relating to the speech domain, it becomes a necessity to have efficient noise removal models with greater efficiency and less complexity. The presence of noise in audio signals poses a great complication when working on speech recognition, enhancement, improvement, and transmission. Hence, there is a necessity to develop the most efficient algorithm for noise reduction which works in real time and is successful in removing maximum noise. To be above this difficulty, this paper presents an efficient algorithm for noise detection which works on the principles of deep learning, specifically convolutional neural networks (CNNs) and the removal of similar noise from the audio using the Python module ‘noise reducer.’
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Dogra, M., Borwankar, S., Domala, J. (2021). Noise Removal from Audio Using CNN and Denoiser. In: Biswas, A., Wennekes, E., Hong, TP., Wieczorkowska, A. (eds) Advances in Speech and Music Technology. Advances in Intelligent Systems and Computing, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-33-6881-1_4
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DOI: https://doi.org/10.1007/978-981-33-6881-1_4
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