Abstract: A method for enhancing telephone speech signals based on Deep Convolutional Neural Network (CNN) is disclosed. The method is able to reduce the effect of acoustic distortions in daily scenarios during a telephone call. It is a single-channel, speech-oriented method with causal design and low latency. The novelty lies in the noise reduction method which, based on the classical gain method, uses a CNN to learn the Wiener estimator. Then, it computes the gain of the filter to enhance the speech power over the noise power for each time-frequency component of the signal. The selection of the Wiener gain estimator as an essential element of the method, decreases the vulnerability to estimation errors since the characteristics of this measure make it very appropriate to be estimated by deep learning approaches.

Abstract: A method for enhancing telephone speech signals based on Deep Convolutional Neural Network (CNN) is disclosed. The method is able to reduce the effect of acoustic distortions in daily scenarios during a telephone call. It is a single-channel, speech-oriented method with causal design and low latency. The novelty lies in the noise reduction method which, based on the classical gain method, uses a CNN to learn the Wiener estimator. Then, it computes the gain of the filter to enhance the speech power over the noise power for each time-frequency component of the signal. The selection of the Wiener gain estimator as an essential element of the method, decreases the vulnerability to estimation errors since the characteristics of this measure make it very appropriate to be estimated by deep learning approaches.