# FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks

arXiv: https://arxiv.org/abs/1902.04891 · HTML: https://mlsanity.com/paper/1902.04891/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

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

- [Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation](https://mlsanity.com/paper/2301.10752/) (2023)
- [Resource-Efficient Separation Transformer](https://mlsanity.com/paper/2206.09507/) (2022)
- [Sandglasset: A Light Multi-Granularity Self-attentive Network For Time-Domain Speech Separation](https://mlsanity.com/paper/2103.00819/) (2021)
- [Effective Low-Cost Time-Domain Audio Separation Using Globally Attentive Locally Recurrent Networks](https://mlsanity.com/paper/2101.05014/) (2021)
- [Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators](https://mlsanity.com/paper/2111.02351/) (2021)
- [GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition](https://mlsanity.com/paper/2210.15834/) (2022)
- [A Neural State-Space Model Approach to Efficient Speech Separation](https://mlsanity.com/paper/2305.16932/) (2023)
- [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)
- [A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References](https://mlsanity.com/paper/2508.14623/) (2025)
- [Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation](https://mlsanity.com/paper/2210.15305/) (2022)
- [Deep neural network techniques for monaural speech enhancement: state of the art analysis](https://mlsanity.com/paper/2212.00369/) (2022)
- [Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement](https://mlsanity.com/paper/2306.08956/) (2023)
- [A Time-domain Real-valued Generalized Wiener Filter for Multi-channel Neural Separation Systems](https://mlsanity.com/paper/2112.03533/) (2021)
- [Online Self-Attentive Gated RNNs for Real-Time Speaker Separation](https://mlsanity.com/paper/2106.13493/) (2021)

## Abstract (arXiv)

Deep dilated temporal convolutional networks (TCN) have been proved to be very effective in sequence modeling. In this paper we propose several improvements of TCN for end-to-end approach to monaural speech separation, which consists of 1) multi-scale dynamic weighted gated dilated convolutional pyramids network (FurcaPy), 2) gated TCN with intra-parallel convolutional components (FurcaPa), 3) weight-shared multi-scale gated TCN (FurcaSh), 4) dilated TCN with gated difference-convolutional component (FurcaSu), that all these networks take the mixed utterance of two speakers and maps it to two separated utterances, where each utterance contains only one speaker's voice. For the objective, we propose to train the network by directly optimizing utterance level signal-to-distortion ratio (SDR) in a permutation invariant training (PIT) style. Our experiments on the the public WSJ0-2mix data corpus results in 18.4dB SDR improvement, which shows our proposed networks can leads to performance improvement on the speaker separation task.