# ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation

arXiv: https://arxiv.org/abs/2408.08729 · HTML: https://mlsanity.com/paper/2408.08729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement](https://mlsanity.com/paper/2010.15508/) (2020)
- [ICASSP 2023 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2303.11510/) (2023)
- [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022)
- [ICASSP 2022 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2202.13288/) (2022)
- [A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/1709.08243/) (2017)
- [Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement](https://mlsanity.com/paper/2106.11789/) (2021)
- [The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks](https://mlsanity.com/paper/2110.09958/) (2021)
- [Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filters](https://mlsanity.com/paper/1904.08369/) (2019)
- [Objective Measures of Perceptual Audio Quality Reviewed: An Evaluation of Their Application Domain Dependence](https://mlsanity.com/paper/2110.11438/) (2021)
- [The Sound Demixing Challenge 2023 unicodex2013 Cinematic Demixing Track](https://mlsanity.com/paper/2308.06981/) (2023)
- [A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation](https://mlsanity.com/paper/2309.02539/) (2023)
- [Deep Multi-Frame Filtering for Hearing Aids](https://mlsanity.com/paper/2305.08225/) (2023)
- [Dialog+ in Broadcasting: First Field Tests Using Deep-Learning-Based Dialogue Enhancement](https://mlsanity.com/paper/2112.09494/) (2021)
- [Better Together: Dialogue Separation and Voice Activity Detection for Audio Personalization in TV](https://mlsanity.com/paper/2303.13453/) (2023)

## Abstract (arXiv)

Dialogue separation involves isolating a dialogue signal from a mixture, such as a movie or a TV program. This can be a necessary step to enable dialogue enhancement for broadcast-related applications. In this paper, ConcateNet for dialogue separation is proposed, which is based on a novel approach for processing local and global features aimed at better generalization for out-of-domain signals. ConcateNet is trained using a noise reduction-focused, publicly available dataset and evaluated using three datasets: two noise reduction-focused datasets (in-domain), which show competitive performance for ConcateNet, and a broadcast-focused dataset (out-of-domain), which verifies the better generalization performance for the proposed architecture compared to considered state-of-the-art noise-reduction methods.