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

Autor:	Casey, Owen, Dave, Rushit, Seliya, Naeem, Boone, Evelyn R Sowells
Rok vydání:	2021
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.02692 Zobrazit plný text záznamu View this record from Arxiv