# Atss-Net: Target Speaker Separation via Attention-based Neural Network

arXiv: https://arxiv.org/abs/2005.09200 · HTML: https://mlsanity.com/paper/2005.09200/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

## Built on by (most cited first, 8 of 8)

- [Self-Supervised Audio-Visual Soundscape Stylization](https://mlsanity.com/paper/2409.14340/) (2024)
- [X-SepFormer: End-to-end Speaker Extraction Network with Explicit Optimization on Speaker Confusion](https://mlsanity.com/paper/2303.05023/) (2023)
- [Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation](https://mlsanity.com/paper/2210.12635/) (2022)
- [Sounding that Object: Interactive Object-Aware Image to Audio Generation](https://mlsanity.com/paper/2506.04214/) (2025)
- [Single microphone speaker extraction using unified time-frequency Siamese-Unet](https://mlsanity.com/paper/2203.02941/) (2022)
- [Sparsely Overlapped Speech Training in the Time Domain: Joint Learning of Target Speech Separation and Personal VAD Benefits](https://mlsanity.com/paper/2106.14371/) (2021)
- [Improving Target Speaker Extraction with Sparse LDA-transformed Speaker Embeddings](https://mlsanity.com/paper/2301.06277/) (2023)
- [Spectron: Target Speaker Extraction using Conditional Transformer with Adversarial Refinement](https://mlsanity.com/paper/2409.01352/) (2024)

## Abstract (arXiv)

Recently, Convolutional Neural Network (CNN) and Long short-term memory (LSTM) based models have been introduced to deep learning-based target speaker separation. In this paper, we propose an Attention-based neural network (Atss-Net) in the spectrogram domain for the task. It allows the network to compute the correlation between each feature parallelly, and using shallower layers to extract more features, compared with the CNN-LSTM architecture. Experimental results show that our Atss-Net yields better performance than the VoiceFilter, although it only contains half of the parameters. Furthermore, our proposed model also demonstrates promising performance in speech enhancement.