Multimedia Noise Reduction Techniques Using Advanced Signal Processing and AI
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Abstract:
Background: Multimedia signals such as imagery and audio have a critical role in a variety of modern applications, including medical, industrial, and digital security fields. However, signal quality often degrades due to noise interference caused by environmental factors and hardware limitations, while conventional signal processing methods still face challenges in handling noise that is non-stationary and complex.
Aims: This study aims to examine and compare the effectiveness of noise reduction techniques based on Advanced Signal Processing (ASP) and Artificial Intelligence (AI), as well as identify research trends, challenges, and development directions in the field of multimedia noise reduction.
Methods: This study uses a literature study method by reviewing 20 international journals indexed by Scopus. The analysis was conducted comparatively based on the methods used, evaluation metrics, and application domains discussed in each study.
Results: The results show that ASP-based methods excel in computing efficiency and real-time performance, but have limitations in handling non-linear noise. In contrast, Deep Learning-based approaches such as Convolutional Neural Networks and Transformers are able to produce higher noise reduction accuracy, but require large data and computing resources. The DSP–AI hybrid model has been proven to be able to balance signal quality and system efficiency.
Conclusion: This study concludes that the integration of advanced signal processing and artificial intelligence is the most prospective solution for building an adaptive, efficient, and reliable multimedia noise reduction system in future applications.
Keywords: Artificial Intelligence , Deep Learning , Hyper DSP-AI , Multimedia Signal Processing , Noise Reduction
Copyright (c) 2026 Abdul Vaiz Vahry Iskandar, Rifqi Reis Ramadhan, Muhammad Farizi, Abdillah Siraj Al Haqqi, Ardi Nugrahanto, Amel Zulfukar Hassan Adlan, Adamu Abubakar Muhammad
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
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