# Complete and separate: Conditional separation with missing target source attribute completion

arXiv: https://arxiv.org/abs/2307.14609 · HTML: https://mlsanity.com/paper/2307.14609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 28 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Deep clustering: Discriminative embeddings for segmentation and separation](https://mlsanity.com/paper/1508.04306/) (2015)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Wavesplit: End-to-End Speech Separation by Speaker Clustering](https://mlsanity.com/paper/2002.08933/) (2020)
- [VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](https://mlsanity.com/paper/1810.04826/) (2018)
- [TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation](https://mlsanity.com/paper/2209.03952/) (2022)
- [Single-Channel Multi-Speaker Separation using Deep Clustering](https://mlsanity.com/paper/1607.02173/) (2016)
- [Separate What You Describe: Language-Queried Audio Source Separation](https://mlsanity.com/paper/2203.15147/) (2022)
- [Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations](https://mlsanity.com/paper/1907.01277/) (2019)
- [Listen to What You Want: Neural Network-based Universal Sound Selector](https://mlsanity.com/paper/2006.05712/) (2020)
- [AudioScopeV2: Audio-Visual Attention Architectures for Calibrated Open-Domain On-Screen Sound Separation](https://mlsanity.com/paper/2207.10141/) (2022)
- [Text-Driven Separation of Arbitrary Sounds](https://mlsanity.com/paper/2204.05738/) (2022)
- [CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos](https://mlsanity.com/paper/2212.07065/) (2022)
- [Improving Universal Sound Separation Using Sound Classification](https://mlsanity.com/paper/1911.07951/) (2019)
- [Compute and memory efficient universal sound source separation](https://mlsanity.com/paper/2103.02644/) (2021)
- [Heterogeneous Target Speech Separation](https://mlsanity.com/paper/2204.03594/) (2022)
- [Improving Sound Event Detection In Domestic Environments Using Sound Separation](https://mlsanity.com/paper/2007.03932/) (2020)
- [Optimal Condition Training for Target Source Separation](https://mlsanity.com/paper/2211.05927/) (2022)
- [Improving Speech Enhancement via Event-based Query](https://mlsanity.com/paper/2302.11558/) (2023)

## Abstract (arXiv)

Recent approaches in source separation leverage semantic information about their input mixtures and constituent sources that when used in conditional separation models can achieve impressive performance. Most approaches along these lines have focused on simple descriptions, which are not always useful for varying types of input mixtures. In this work, we present an approach in which a model, given an input mixture and partial semantic information about a target source, is trained to extract additional semantic data. We then leverage this pre-trained model to improve the separation performance of an uncoupled multi-conditional separation network. Our experiments demonstrate that the separation performance of this multi-conditional model is significantly improved, approaching the performance of an oracle model with complete semantic information. Furthermore, our approach achieves performance levels that are comparable to those of the best performing specialized single conditional models, thus providing an easier to use alternative.