# Sudo rm -rf: Efficient Networks for Universal Audio Source Separation

arXiv: https://arxiv.org/abs/2007.06833 · HTML: https://mlsanity.com/paper/2007.06833/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 30 of 51)

- [Dual-path Mamba: Short and Long-term Bidirectional Selective Structured State Space Models for Speech Separation](https://mlsanity.com/paper/2403.18257/) (2024)
- [An efficient encoder-decoder architecture with top-down attention for speech separation](https://mlsanity.com/paper/2209.15200/) (2022)
- [SepPrune: Structured Pruning for Efficient Deep Speech Separation](https://mlsanity.com/paper/2505.12079/) (2025)
- [Zero-shot Audio Source Separation through Query-based Learning from Weakly-labeled Data](https://mlsanity.com/paper/2112.07891/) (2021)
- [MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions](https://mlsanity.com/paper/2302.11824/) (2023)
- [RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing](https://mlsanity.com/paper/2202.08862/) (2022)
- [Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing](https://mlsanity.com/paper/2407.15580/) (2024)
- [Resource-Efficient Separation Transformer](https://mlsanity.com/paper/2206.09507/) (2022)
- [TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation](https://mlsanity.com/paper/2410.01469/) (2024)
- [An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits](https://mlsanity.com/paper/2212.10744/) (2022)
- [Subnetwork-to-go: Elastic Neural Network with Dynamic Training and Customizable Inference](https://mlsanity.com/paper/2312.03464/) (2023)
- [Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data](https://mlsanity.com/paper/2105.04727/) (2021)
- [Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators](https://mlsanity.com/paper/2111.02351/) (2021)
- [Hybrid Neural Networks for On-device Directional Hearing](https://mlsanity.com/paper/2112.05893/) (2021)
- [REAL-M: Towards Speech Separation on Real Mixtures](https://mlsanity.com/paper/2110.10812/) (2021)
- [MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training](https://mlsanity.com/paper/2202.03875/) (2022)
- [Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation](https://mlsanity.com/paper/2208.10325/) (2022)
- [Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning](https://mlsanity.com/paper/2308.09514/) (2023)
- [Continual self-training with bootstrapped remixing for speech enhancement](https://mlsanity.com/paper/2110.10103/) (2021)
- [A Neural State-Space Model Approach to Efficient Speech Separation](https://mlsanity.com/paper/2305.16932/) (2023)
- [Neural Blind Source Separation and Diarization for Distant Speech Recognition](https://mlsanity.com/paper/2406.08396/) (2024)
- [RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge](https://mlsanity.com/paper/2409.08839/) (2024)
- [SepMamba: State-space models for speaker separation using Mamba](https://mlsanity.com/paper/2410.20997/) (2024)
- [Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain](https://mlsanity.com/paper/2110.04791/) (2021)
- [Exploring Self-Attention Mechanisms for Speech Separation](https://mlsanity.com/paper/2202.02884/) (2022)
- [SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios](https://mlsanity.com/paper/2410.01481/) (2024)
- [A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References](https://mlsanity.com/paper/2508.14623/) (2025)
- [BLOOM-Net: Blockwise Optimization for Masking Networks Toward Scalable and Efficient Speech Enhancement](https://mlsanity.com/paper/2111.09372/) (2021)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals](https://mlsanity.com/paper/2303.06438/) (2023)

## Abstract (arXiv)

In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRMRF) as well as their aggregation which is performed through simple one-dimensional convolutions. In this way, we are able to obtain high quality audio source separation with limited number of floating point operations, memory requirements, number of parameters and latency. Our experiments on both speech and environmental sound separation datasets show that SuDoRMRF performs comparably and even surpasses various state-of-the-art approaches with significantly higher computational resource requirements.