Denoising AutoEncoder - Documentation

A PyTorch implementation of a Denoising Autoencoder (DAE) for single-channel speech enhancement, following the approach of Lu et al. (2013) and with architecture choices informed by Nossier et al. (2020).

The model learns a direct mapping from noisy spectral features to clean spectral features. At inference time only the noisy side is available: the encoder compresses it into a bottleneck, and the decoder reconstructs a clean estimate from that bottleneck.

Noisy speech frame                    Clean speech estimate
        │                                      ▲
        ▼                                      │
 ┌─────────────┐    bottleneck z    ┌──────────────┐
 │   Encoder   │ ─────────────────► │    Decoder   │
 └─────────────┘                    └──────────────┘

Getting started

• Overview
• Installation
• Quick Start

API Reference

• Network Architectures
  • VanillaAutoEncoder
  • WaveUNet
• Data Utilities
  • Feature Extraction — libdse.data.features
  • LibriSpeech Dataset — libdse.data.librispeech
  • DEMAND Noise Dataset — libdse.data.noise
  • Exceptions — libdse.data.err
• Training Scripts
  • Log-magnitude — simpleAE_logmag_nc
• Gradio Demo
  • Log-magnitude demo — simpleAE_logmag_nc
• Evaluation Metrics

References

• Lu, X., Tsao, Y., Matsuda, S., & Hori, C. (2013). Speech Enhancement Based on Deep Denoising Autoencoder. INTERSPEECH 2013.

• Nossier, Soha A., et al. An experimental analysis of deep learning architectures for supervised speech enhancement. _Electronics 10.1_ (2020): 17.

• Thiemann, Joachim, Nobutaka Ito, and Emmanuel Vincent. The diverse environments multi-channel acoustic noise database (demand): A database of multichannel environmental noise recordings. _Proceedings of Meetings on Acoustics. Vol. 19. No. 1. Acoustical Society of America_, 2013.