Optimizing Speech Enhancement with Metricgan: A Black-Box Approach

In this article, we present a new approach to speech enhancement using generative adversarial networks (GANs). Our proposed method, called Metricgan, optimizes black-box metrics for speech enhancement by using GANs to generate high-quality speech signals. These generated signals are then used to train a machine learning model that predicts the original clean speech from the noisy input.
The key idea behind Metricgan is to use GANs to generate a set of realistic and diverse speech samples, which are then used as inputs to a machine learning model. The goal is to find the optimal parameters for the machine learning model such that it can accurately predict the original clean speech from the noisy input. By using GANs to generate high-quality speech signals, we can avoid the need for manual feature engineering or complex signal processing techniques.
To train the Metricgan network, we use a combination of reconstruction loss and black-box metrics. The reconstruction loss encourages the model to reconstruct the original clean speech from the noisy input, while the black-box metrics evaluate the quality of the generated speech samples. We use a variety of black-box metrics, including STOI (Signal-to-Noise Ratio in the Intermediate Frequencies), SDR (Short-time Objective Intelligibility), and NMOS (Naturalness and Masque Optimality Score).
We evaluate the performance of Metricgan on several benchmark datasets, including LibriSpeech and CALLHOME. Our results show that Metricgan outperforms existing speech enhancement methods in terms of both objective metrics and subjective evaluations. We also demonstrate the versatility of our approach by applying it to different types of noise and environments.
In summary, Metricgan is a promising new approach to speech enhancement that uses GANs to generate high-quality speech signals and optimizes black-box metrics for speech enhancement. Our proposed method has several advantages over existing methods, including its ability to handle complex noise environments and its simplicity in implementation.