Noise reduction is crucial to improving real-time acoustic speech communication quality and intelligibility. We present a low-complexity adaptive noise cancellation system based on the Least Mean Squares (LMS) algorithm in a Digital Signal Processing (DSP) environment. The main objective of this research is to reduce t...
Environmental noise can substantially affect speech quality and intelligibility in real-time audio systems, particularly when speech enhancement is implemented on computationally limited edge platforms. Conventional frequency-domain techniques, including spectral subtraction and Wiener filtering, provide computationall...
This paper presents a scheme of Normalized Least Mean Square (NLMS) adaptive filters, implemented in time and frequency domains, for real-time Acoustic Echo Cancellation (AEC) applied on the embedded system, in this case is Raspberry Pi. Recognizing the inherent limitations of linear adaptive filtering in complex acous...
Noise suppression that improves speech intelligibility is critical in teleconferencing, Voice over Internet Protocol (VoIP), hearing aids, and speech recognition systems. This work has attempted an adaptive filtering technique for noise suppression to improve the performance of speech signals. In this regard, two of th...
In practical voice communication and processing, speech signals are extremely sensitive to background noise, equipment noise, and interference from complex environments, leading to a decline in speech quality and clarity. This, in turn, affects the overall performance of downstream systems such as speech recognition, s...
Overall, this review provides a unified perspective on speech enhancement techniques, identifies their strengths, and highlights emerging research opportunities for the development of robust, efficient, and intelligent speech enhancement systems.
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