EchoFree: AI Powered Audio Cleaner

Abstract

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 pre-recorded 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 batch-based 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.

Introduction

Audio-based communication is essential in modern digital applications such as online meetings, virtual classrooms, podcasts, interviews, surveillance, and smart systems. Clear speech is crucial not only for human interaction but also for automated systems like Automatic Speech Recognition (ASR), speaker verification, and audio analytics. However, real-world recordings are often degraded by background noise, reverberation, and acoustic distortions, which reduce intelligibility and system performance.

Evolution of Speech Enhancement

Speech enhancement research has evolved significantly:

1. Classical DSP Techniques
   Early methods such as spectral subtraction and Wiener filtering were computationally efficient but relied on assumptions like stationary noise. In real environments, these assumptions fail, leading to artifacts (e.g., musical noise) and poor generalization.

2. Deep Learning-Based Models
   CNNs, RNNs, Temporal Convolutional Networks (TCNs), and complex-domain models (e.g., DCCRN) improved performance by learning non-linear mappings between noisy and clean speech. These models better handle dynamic noise but often require high computational resources.

3. Generative and Diffusion Models
   GAN-based systems (e.g., SEGAN) and diffusion models (e.g., LDMSE) enhance perceptual quality and naturalness by reconstructing fine spectral and temporal details. Despite superior quality, they are computationally expensive and slow for long recordings.

4. Hybrid Frameworks
   Recent research integrates pretrained deep models with lightweight autoencoders and classical filtering to balance quality and efficiency. Hybrid systems aim to achieve robust denoising while remaining scalable and practical.

Key Challenges in Existing Systems

Despite progress, several limitations remain:

• Difficulty handling non-stationary and complex noise

• Trade-off between enhancement quality and computational efficiency

• Poor scalability for long-duration audio

• Inconsistent tone preservation and temporal continuity

• Limited batch-based processing strategies for offline recordings

Most systems focus on real-time or short-segment enhancement, making them less suitable for long pre-recorded lectures, interviews, or multimedia archives.

Research Gap

The main research gaps identified are:

1. Lack of scalable frameworks for long-duration offline audio.

2. Imbalance between high-fidelity enhancement and computational efficiency.

3. Insufficient preservation of natural speech tone.

4. Limited hybrid architectures combining pretrained models with lightweight refinement.

5. Minimal exploration of batch-based processing pipelines.

Proposed Solution: EchoFree Hybrid Framework

To address these gaps, the text introduces EchoFree, an AI-powered hybrid speech enhancement system designed for post-processing pre-recorded audio and video.

Core Characteristics

• Modular pipeline architecture

  • Audio extraction and preprocessing

  • Hybrid noise reduction (deep learning + classical filtering)

  • Post-processing and reconstruction

• Hybrid Model Design

  • Autoencoder-based compact representations

  • CNN/Transformer-based feature extraction

  • Classical spectral processing for residual noise

• Batch-Based Processing

  • Long audio divided into overlapping segments

  • Independent processing of batches

  • Overlap-add reconstruction for smooth continuity

• Natural Tone Preservation

  • Perceptual frequency weighting

  • Balanced loss functions

  • Adaptive smoothing to avoid over-suppression

Conclusion

The key contributions and findings of this work are: 1) Hybrid Model Efficiency: EchoFree integrates autoencoder networks, CNN + Transformer layers, and classical spectral subtraction to enhance speech quality, demonstrating superior performance over baseline and existing state-of-the-art methods. 2) Long Audio Processing: Batch-based processing allows efficient handling of long-duration audio/video files without compromising continuity or tonal quality. 3) Natural Tone Preservation: Advanced post-processing techniques maintain the natural timbre and intelligibility of speech, confirmed by both objective metrics (PESQ, STOI, SNR) and qualitative analysis. 4) Robustness to Noise: The system effectively suppresses stationary and non-stationary noise types, making it suitable for diverse real-world scenarios including educational videos, podcasts, interviews, and multimedia content. In summary, EchoFree provides a comprehensive solution for enhancing pre-recorded audio and video, achieving measurable improvements in speech quality and listener experience.

References

[1] X. Chao, N. Li, and M. Zhou, “Universal Speech Enhancement with Regression and Generative Mamba,” arXiv preprint arXiv:2501.11234, 2025. [2] A. Hamadouche, M. Benali, and R. Adjoudj, “Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies,” arXiv preprint arXiv:2502.04411, 2025. [3] M. Khondkar, F. Ali, and M. Hasan, “Comparative Evaluation of Deep Learning Models for Real-World Speech Enhancement,” arXiv preprint arXiv:2503.00521, 2025. [4] M. Medani, V. Patel, and S. Kumar, “End-to-End Feature Fusion for Jointly Optimized Speech Enhancement and ASR,” Scientific Reports, vol. 15, no. 66, pp. 1–14, 2025. [5] S. Natarajan, K. Rao, and R. Kulkarni, “Deep Neural Networks for Speech Enhancement and Recognition: A Systematic Review,” Ain Shams Engineering Journal, vol. 16, no. 2, pp. 556–572, 2025. [6] T. Sato, K. Nakamura, and H. Arai, “Generic Speech Enhancement with Self-Supervised Representation Space Loss,” Frontiers in Signal Processing, vol. 9, pp. 1–12, 2025. [7] A. Ullah, I. Shah, and J. Kim, “Multimodal Learning-Based Speech Enhancement and Separation,” Information Fusion, vol. 99, pp. 101–119, 2025. [8] A. Rao and P. Singh, “aTENNuate: State-Space Autoencoder for Real-Time On-Device Speech Denoising,” arXiv preprint arXiv:2501.07865, 2025. [9] R. Saini, D. Patel, and P. Verma, “Systematic Literature Review of Speech Enhancement Algorithms,” Electronics, vol. 14, no. 5, 2025. [10] D. Kim, S. Park, and J. Lee, “LDMSE: Low-Dimensional Diffusion Speech Enhancement,” APSIPA Transactions on Signal and Information Processing, vol. 13, no. 2, pp. 115–128, 2024 [11] Q. Nguyen, H. Li, and A. Zhou, “DAVSE: Diffusion-Based Audio-Visual Speech Enhancement,” in Proc. AVSEC, 2024, pp. 122–131. [12] Z. Huang, Y. Zhang, Q. Wang, and B. Xu, “Transformer-Based Diffusion Models for End-to-End Speech Enhancement,” arXiv preprint arXiv:2304.02112, 2023. [13] J. Li, J. Li, P. Wang, and Y. Zhang, “DCHT: Deep Complex Hybrid Transformer for Speech Enhancement,” arXiv preprint arXiv:2310.19602, 2023. [14] B. Bahmei, S. Arzanpour, and E. Birmingham, “Real-Time Speech Enhancement via a Hybrid ViT: A Dual-Input Acoustic-Image Feature Fusion,” arXiv preprint arXiv:2511.11825, 2025. [15] Y.-X. Lu, Y. Ai, and Z.-H. Ling, “MP-SENet: Parallel Denoising of Magnitude and Phase Spectra for Speech Enhancement,” arXiv preprint arXiv:2305.13686, 2023. [16] Y. Cao, S. Xu, W. Zhang et al., “Hybrid Lightweight Temporal-Frequency Analysis Network for Multi-Channel Speech Enhancement,” EURASIP Journal on Audio, Speech, and Music Processing, 2025. [17] “Time-Domain Speech Enhancement with CNN and Time-Attention Transformer,” Digital Signal Processing, vol. 147, 2024. [18] Y. Kim and H.-S. Kim, “Deep Learning-Driven Speech and Audio Processing: Advances in Noise Reduction and Real-Time Voice Analytics,” National Journal of Speech and Audio Processing, 2025 [19] H. Zhang, L. Wang, and M. Chen, “End-to-End Neural Speech Enhancement with Dual-Path Temporal Convolutions,” IEEE Access, vol. 11, pp. 21542–21556, 2023. [20] F. Ali, M. Hasan, and K. Li, “Multi-Channel Speech Denoising Using Hybrid Spectrogram-Waveform Models,” Sensors, vol. 25, no. 3, pp. 1101–1115, 2025. [21] S. Kim, J. Park, and H. Lee, “Audio-Visual Fusion for Robust Speech Enhancement in Real-World Environments,” IEEE Transactions on Multimedia, 2024. [22] Y. Wu, L. Sun, and H. Zhang, “Self-Supervised Speech Enhancement with Generative Diffusion Models,” Frontiers in Signal Processing, 2025 [23] A. Das, S. Roy, and P. Gupta, “Hybrid Autoencoder-CNN Architecture for Noise-Robust Speech Processing,” Journal of AI and Signal Processing, 2024. [24] T. Li, H. Chen, and S. Wang, “Temporal Attention Transformers for Long-Duration Speech Enhancement,” IEEE Transactions on Audio, Speech, and Language Processing, 2025. [25] R. Singh, V. Kumar, and P. Yadav, “Multi-Domain Evaluation of Hybrid Speech Enhancement Models for Pre-Recorded Content,” Journal of Acoustic Engineering, 2025. [26] M. Liu, Y. He, and K. Zhang, “Perceptually Guided Hybrid Speech Enhancement Using Deep Learning and Spectral Methods,” IEEE Access, 2024.

Copyright

Copyright © 2026 Sanya Sonker, Mahiman Singh Deopa, Dr. Diwakar Yagyasen . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.