# C-EMDNet: A Nonlinear Morphological Deep Framework for Robust Speech Enhancement

## TL;DR

C-EMDNet is a new deep learning method for improving speech clarity by using adaptive decomposition and a time-based neural network.

## Contribution

The novel framework combines CEEMDAN decomposition with a U-Net-like architecture for speech enhancement in the IMF domain.

## Key findings

- C-EMDNet outperforms classical and deep learning baselines on standard noisy speech datasets.
- The approach preserves harmonic and formant structures while suppressing noise effectively.

## Abstract

This study introduces C-EMDNet, a nonlinear speech denoising approach that combines the adaptive decomposition capabilities of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a deep convolutional architecture operating directly in the time-intrinsic mode function (IMF) domain. Unlike conventional enhancement methods that rely on fixed time–frequency representations, such as the short-time Fourier transform (STFT), the proposed approach interprets CEEMDAN IMFs as a morphological latent space that captures the multi-scale structure of speech. A U-Net-like network was trained to estimate mode-wise masks, enabling selective noise suppression while preserving the harmonic and formant structures. Experiments on standard noisy speech datasets show that C-EMDNet outperforms classical denoising algorithms and competitive deep learning baselines. These results highlight the promise of nonlinear morphological representations for an alternative framework speech enhancement.