Voice-selective sound processing, focusing specifically on the extraction of singular audio signals from multifaceted soundscapes, has rapidly gained prominence in a diverse range of applications from health diagnostics to contemporary smart home integrations. The pressing need for such precision extraction arises from our increasingly cluttered audio environments and the escalating importance of clean, distinct voice signals for efficient processing and analysis. The present research delves deep into the intricate mechanisms that drive voice-selective sound processing. By amalgamating the nuanced understanding of human auditory models with the prowess of cutting-edge machine learning methodologies, a unique framework is proposed. This innovative model promises a marked improvement in voice extraction capabilities even in environments characterized by high ambient noise levels. This paper further elucidates the key components of this system, offering insights into its potential and effectiveness in real-world applications.
Authors: Shikha Sharma, Vijay Prakash Sharma, Sandeep Gupta
DOI: https://doi.org/10.1145/3647444.3647831
Publish Year: 2023
