# Multi-scale Multi-band DenseNets for Audio Source Separation

arXiv: https://arxiv.org/abs/1706.09588 · HTML: https://mlsanity.com/paper/1706.09588/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 15 of 15)

- [Move2Hear: Active Audio-Visual Source Separation](https://mlsanity.com/paper/2105.07142/) (2021)
- [FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement](https://mlsanity.com/paper/2203.12188/) (2022)
- [AnyTalker: Scaling Multi-Person Talking Video Generation with Interactivity Refinement](https://mlsanity.com/paper/2511.23475/) (2025)
- [A Stem-Agnostic Single-Decoder System for Music Source Separation Beyond Four Stems](https://mlsanity.com/paper/2406.18747/) (2024)
- [Audiovisual Singing Voice Separation](https://mlsanity.com/paper/2107.00231/) (2021)
- [Short-Term Memory Convolutions](https://mlsanity.com/paper/2302.04331/) (2023)
- [Multi-Dimensional and Multi-Scale Modeling for Speech Separation Optimized by Discriminative Learning](https://mlsanity.com/paper/2303.03737/) (2023)
- [A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments](https://mlsanity.com/paper/2303.03732/) (2023)
- [Improving Music Source Separation with Diffusion and Consistency Refinement](https://mlsanity.com/paper/2412.06965/) (2024)
- [Pitch-Informed Instrument Assignment Using a Deep Convolutional Network with Multiple Kernel Shapes](https://mlsanity.com/paper/2107.13617/) (2021)
- [LightSAFT: Lightweight Latent Source Aware Frequency Transform for Source Separation](https://mlsanity.com/paper/2111.12516/) (2021)
- [Effects of Dataset Sampling Rate for Noise Cancellation through Deep Learning](https://mlsanity.com/paper/2405.20884/) (2024)
- [Improving Real-Time Music Accompaniment Separation with MMDenseNet](https://mlsanity.com/paper/2407.00657/) (2024)
- [Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions](https://mlsanity.com/paper/2508.14556/) (2025)
- [MultiLinguahah : A New Unsupervised Multilingual Acoustic Laughter Segmentation Method](https://mlsanity.com/paper/2605.06309/) (2026)

## Abstract (arXiv)

This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet), which has shown excellent results on image classification tasks. To deal with the specific problem of audio source separation, an up-sampling layer, block skip connection and band-dedicated dense blocks are incorporated on top of DenseNet. The proposed approach takes advantage of long contextual information and outperforms state-of-the-art results on SiSEC 2016 competition by a large margin in terms of signal-to-distortion ratio. Moreover, the proposed architecture requires significantly fewer parameters and considerably less training time compared with other methods.