Audio Denoising Using Deep Neural Networks

Improving speech quality is becoming a basic requirement with increasing interest in speech processing applications. A lot of speech enhancement techniques are developed to reduce or completely remove listeners fatigue from various devices like smartphones and also from online communication applications. Background noise often interrupts communication, and this was solved using a hardware physical device that normally emits a negative frequency of the incoming audio noise signal to cancel out the noise. Deep learning has recently made a break-through in the speech enhancement process. This paper proposes an audio denoising model which is built on a deep neural network architecture based on spectrograms (which is a hybrid between frequency domain and time domain). The proposed deep neural network model effectively predicts the negative noise frequency for given input incoming audio file with noise. After prediction, the predicted values are then removed from the original noise audio file to create the denoised audio output.