EchoFree: AI Powered Audio Cleaner

Audio quality plays a crucial role in modern digital communication and multimedia applications, including online meetings, recorded lectures, podcasts, interviews, and video content creation. In practical recording environments, audio signals are frequently captured under uncontrolled conditions, where background noise, reverberation, and acoustic interference mix with the original speech, resulting in degraded intelligibility and perceptual quality. Conventional speech enhancement methods based on classical signal processing rely on fixed assumptions regarding noise characteristics and often perform poorly in dynamic and non-stationary environments. Although recent advancements in deep learning have significantly improved speech enhancement through data-driven approaches, many existing solutions exhibit high computational complexity, limited scalability for long-duration recordings, and insufficient preservation of natural speech characteristics, particularly for prerecorded audio and video content. This review paper presents a comprehensive analysis of state-of-the-art speech enhancement techniques reported between 2018 and 2025, encompassing classical approaches, deep neural networks, generative models, diffusion-based frameworks, and hybrid architectures. Additionally, the paper discusses a conceptual hybrid enhancement framework, referred to as EchoFree, which integrates a pretrained high-fidelity model with a custom autoencoder and batchbased processing to achieve effective noise suppression while maintaining speech naturalness and processing efficiency. Key research gaps related to real-world deployment, long-audio scalability, and perceptual quality preservation are identified, and potential future research directions are outlined to support the development of robust and scalable AI-powered audio cleaning systems.