The Research Compared Deep Learning Models on Noisy Speech, Weighing Noise Suppression, Naturalness, and Processing Speed

Clear Voice, a Turkish AI-powered audio processing platform born from an academic paper, cleans background noise from recordings directly in the browser. Developed by Gazi University computer engineering student Muhammed Emin Korkut, the tool targets podcasts, meetings, interviews, and educational recordings, reducing noise like hum, wind, electrical crackle, and room echo while preserving speech clarity and naturalness. The project stems from Korkut's paper presented at the 34th IEEE Signal Processing and Communications Applications Conference (SIU 2026), titled "Comparative Analysis of Deep Learning Models for Speech Enhancement in Noisy Environments.

The research compared deep learning models on noisy speech, weighing noise suppression, naturalness, and processing speed. Instead of applying one model to all files, Korkut built a multi-model platform. "Clear Voice emerged directly from turning academic research into a product, he said.

I compared different deep learning models' performance on noisy recordings. I saw each model excelled under different conditions. Rather than leaving these results in a paper, I wanted to turn them into an accessible platform for daily use.

The platform offers five models (V1 to V5) with varying processing power and enhancement characteristics, letting users match the tool to their recording's nature—for instance, a steady air conditioner hum versus traffic and wind in an outdoor interview. Users upload audio, pick a model, and receive a cleaned output they can compare with the original before downloading. The web-based service supports MP3, WAV, M4A, and FLAC formats.

Use cases include reducing background noise in home-recorded podcasts, cleaning traffic and wind from field interviews, clarifying online meeting recordings, reducing room echo in educational videos, and preparing voiceovers and archival recordings for publication. The platform targets content creators, journalists, students, educators, and small teams who want to avoid complex filters in professional editing software.