Machine Learning: Challenges, Limitations, and Compatibility for Audio Restoration Processes

TL;DR

This paper explores the use of machine learning, specifically GANs, for restoring degraded speech audio, highlighting challenges with implementation and compatibility issues in current models.

Contribution

It investigates the application of GAN-based models for speech restoration and discusses the practical challenges faced in training and deploying these models.

Findings

Compatibility issues hinder model development

Degraded speech can be partially restored using GANs

Implementation challenges limit practical deployment

Abstract

In this paper machine learning networks are explored for their use in restoring degraded and compressed speech audio. The project intent is to build a new trained model from voice data to learn features of compression artifacting distortion introduced by data loss from lossy compression and resolution loss with an existing algorithm presented in SEGAN: Speech Enhancement Generative Adversarial Network. The resulting generator from the model was then to be used to restore degraded speech audio. This paper details an examination of the subsequent compatibility and operational issues presented by working with deprecated code, which obstructed the trained model from successfully being developed. This paper further serves as an examination of the challenges, limitations, and compatibility in the current state of machine learning.