Machine Learning: Challenges, Limitations, and Compatibility for Audio Restoration Processes
This is an incremental study highlighting practical challenges in applying existing machine learning methods to audio restoration, relevant for researchers and practitioners in audio processing.
The paper tackled the problem of restoring degraded and compressed speech audio using machine learning, but the result was that compatibility and operational issues with deprecated code obstructed the successful development of a trained model.
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.