# DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering

arXiv: https://arxiv.org/abs/2110.05588 · HTML: https://mlsanity.com/paper/2110.05588/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Interspeech 2021 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2101.01902/) (2021)
- [A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/1709.08243/) (2017)
- [Towards efficient models for real-time deep noise suppression](https://mlsanity.com/paper/2101.09249/) (2021)
- [A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech](https://mlsanity.com/paper/2008.04259/) (2020)
- [DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement](https://mlsanity.com/paper/2106.08672/) (2021)
- [Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filters](https://mlsanity.com/paper/1904.08369/) (2019)
- [CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding](https://mlsanity.com/paper/2001.10218/) (2020)
- [Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based On PercepNet](https://mlsanity.com/paper/2102.05245/) (2021)
- [Phasebook and Friends: Leveraging Discrete Representations for Source Separation](https://mlsanity.com/paper/1810.01395/) (2018)
- [Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/2006.13067/) (2020)

## Built on by (most cited first, 30 of 38)

- [DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement](https://mlsanity.com/paper/2305.08227/) (2023)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (2022)
- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [Ultra Low Complexity Deep Learning Based Noise Suppression](https://mlsanity.com/paper/2312.08132/) (2023)
- [Dynamic Gated Recurrent Neural Network for Compute-efficient Speech Enhancement](https://mlsanity.com/paper/2408.12425/) (2024)
- [Real-Time Audio-Visual Speech Enhancement Using Pre-trained Visual Representations](https://mlsanity.com/paper/2507.21448/) (2025)
- [A Mask Free Neural Network for Monaural Speech Enhancement](https://mlsanity.com/paper/2306.04286/) (2023)
- [SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement](https://mlsanity.com/paper/2509.24708/) (2025)
- [Beyond Transcripts: A Renewed Perspective on Audio Chaptering](https://mlsanity.com/paper/2602.08979/) (2026)
- [Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation](https://mlsanity.com/paper/2303.00529/) (2023)
- [Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression](https://mlsanity.com/paper/2308.11053/) (2023)
- [LMFCA-Net: A Lightweight Model for Multi-Channel Speech Enhancement with Efficient Narrow-Band and Cross-Band Attention](https://mlsanity.com/paper/2502.11462/) (2025)
- [A Two-Stage Hierarchical Deep Filtering Framework for Real-Time Speech Enhancement](https://mlsanity.com/paper/2506.01023/) (2025)
- [Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge](https://mlsanity.com/paper/2404.11116/) (2024)
- [A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices](https://mlsanity.com/paper/2503.21335/) (2025)
- [Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns](https://mlsanity.com/paper/2509.05079/) (2025)
- [Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement](https://mlsanity.com/paper/2601.14925/) (2026)
- [TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting](https://mlsanity.com/paper/2603.17436/) (2026)
- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement](https://mlsanity.com/paper/2608.20971/) (2026)
- [Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction](https://mlsanity.com/paper/2306.15942/) (2023)
- [Efficient Monaural Speech Enhancement using Spectrum Attention Fusion](https://mlsanity.com/paper/2308.02263/) (2023)
- [Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning](https://mlsanity.com/paper/2309.09270/) (2023)
- [Perceptual Noise-Masking with Music through Deep Spectral Envelope Shaping](https://mlsanity.com/paper/2502.17527/) (2025)
- [UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search](https://mlsanity.com/paper/2503.00340/) (2025)
- [RoVo: Robust Voice Protection Against Unauthorized Speech Synthesis with Embedding-Level Perturbations](https://mlsanity.com/paper/2505.12686/) (2025)
- [FDC-Net: Rethinking the association between EEG artifact removal and multi-dimensional affective computing](https://mlsanity.com/paper/2508.05231/) (2025)
- [IS3 : Generic Impulsive--Stationary Sound Separation in Acoustic Scenes using Deep Filtering](https://mlsanity.com/paper/2509.02622/) (2025)
- [Smark: A Watermark for Text-to-Speech Diffusion Models via Discrete Wavelet Transform](https://mlsanity.com/paper/2512.18791/) (2025)
- [Dual-View Predictive Diffusion: Lightweight Speech Enhancement via Spectrogram-Image Synergy](https://mlsanity.com/paper/2602.00568/) (2026)

## Abstract (arXiv)

Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usually preferred over real-valued masks due to their ability to modify the phase. Recent work proposed to use a complex filter instead of a point-wise multiplication with a mask. This allows to incorporate information from previous and future time steps exploiting local correlations within each frequency band. In this work, we propose DeepFilterNet, a two stage speech enhancement framework utilizing deep filtering. First, we enhance the spectral envelope using ERB-scaled gains modeling the human frequency perception. The second stage employs deep filtering to enhance the periodic components of speech. Additionally to taking advantage of perceptual properties of speech, we enforce network sparsity via separable convolutions and extensive grouping in linear and recurrent layers to design a low complexity architecture. We further show that our two stage deep filtering approach outperforms complex masks over a variety of frequency resolutions and latencies and demonstrate convincing performance compared to other state-of-the-art models.