Deep Recurrent Networks for Separation and Recognition of Single-Channel Speech in Nonstationary Background Audio

By Hakan Erdogan, John R. Hershey, Shinji Watanabe, Jonathan Le Roux. 2017

43 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Jinyu Li
2. Zheng-Hua Tan
3. Daniel Michelsanti
4. Sanyuan Chen
5. Shujie Liu
6. Yu Wu
7. Zhuo Chen
8. Naoyuki Kanda

In Signal Processing

1. Jinyu Li
2. Zhuo Chen
3. Dong Yu
4. Takuya Yoshioka
5. Shujie Liu
6. Yu Wu
7. Shinji Watanabe
8. Sanyuan Chen

Citations in the corpus

• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (2021, methodology): “Following [69] and [70], instead of directly predicting the source STFTs, we firstly estimate a group of masks {Ms}s=1 with a deep learning model, and then obtain each source STFT with Xs = Ms Y, where is an elementwise product.”
• An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation (2020, background): “The reason is that a mask is easier to estimate with a neural network [59], [179], [267].”
• Ultra Fast Speech Separation Model with Teacher Student Learning (2021, background): “Following [11, 12], instead of directly outputting the STFT of the individual signals [X1(t, f) .”
• Sergan: Speech Enhancement Using Relativistic Generative Adversarial Networks with Gradient Penalty (2019, background): “Recently, deep neural network (DNN)-based approaches gained success in speech enhancement due to their powerful modeling capabilities [1–5].”