@INPROCEEDINGS{rahat-futureaudio,
author={M. {Rahat-uz-Zaman} and S. {Hye}},
booktitle={2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE)},
title={Prediction of Apple Diseases Using Multiclass Support Vector Machine},
year={2020},
volume={},
number={},
pages={1-5},
abstract={Abstract—Signal processing is a vast subfield of electrical and computer science where audio signal processing has secured a remarkable position to restore corrupted or missing audio blocks. However, generating possible future audio block from the previous audio block is still a new idea that can help to reduce both audio noise and partially missing an audio segment. In this paper, a generative adversarial network (GAN) along with a pipeline is proposed for the prediction of possible audio after an input audio sequence. The proposed model uses short-time Fourier transformation of audio to make it an image. The image is then fed to a conditional GAN to predict the output image. After that, Inverse short-time Fourier transform is then applied to that predicted image, generating the predicted audio sequence. For a small audio sequence prediction, the proposed methodology is quite fast, robust and has achieved a loss of 0.43. So it is may work well if deployed on a voice call and broadcasting applications.},
keywords={Audio;Short Time Fourier Transform;Conditional Generative Adversarial Network},
doi={},
ISSN={},
month={Dec}
}