Advancements in Noise Removal: A Review of Traditional and AI-Driven Audio Signal Processing Techniques - Mukhopadhya, Nilav Darsan; Panchanan, Suparna; Banerjee, Ranjan; Kowar, Subhadip; Mukherjee, Debmalya; Mahabuba Islam, Most (Journal of Neonatal Surgery, 2025)
Noise removal is one of the more prominent research areas of signal processing. Its diversified application scope from diverse falls into the list under speech enhancements, restorations of music, telecommunications aids, and a hearing aid, which reduced unwanted noises and developed through minimal deterioration of its original methods over time. There is a review of the traditional techniques as well as some of the modern techniques that include spectral subtraction, Wiener filtering, and adaptive filtering. Deep learning-based methods in recent years among such approaches are convolutional neural networks and recurrent neural networks. These discussions include capabilities and limitations related to computation efficiency, real-time processing, and effectiveness in the presence of noisy environments. In conclusion, there is a presentation of future directions for research through the use of hybrid models as well as the artificial intelligence-driven approach to eliminate noise for better and adaptive applications.