Parallel Local and Global Context Modeling of Deep Learning-Based Monaural Speech Source Separation Techniques
The novel deep learning-based time domain single channel speech source separation methods have shown remarkable progress. Recent studies achieve either successful global or local context modeling for monaural speaker separation. Existing CNN-based methods perform local context modeling, and RNN-base...
Saved in:
| Main Authors: | Swati Soni, Lalita Gupta, Rishav Dubey |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10969763/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multiscale Convolutional Fusion Network for Efficient Monaural Speech Separation
by: Rui Yang, et al.
Published: (2025-01-01) -
Multipath-Assisted Smartphone Tracking Using a Single Speaker and a Built-In Monaural Microphone
by: Ibuki Yoshida, et al.
Published: (2025-01-01) -
Privacy-Preserving Deep Speaker Separation for Smartphone-Based Passive Speech Assessment
by: Apiwat Ditthapron, et al.
Published: (2021-01-01) -
The Speaker Identification Model for Air-Ground Communication Based on a Parallel Branch Architecture
by: Weijun Pan, et al.
Published: (2025-03-01) -
Digital Pre-Emphasis Compensator for Loudspeaker on the Inverse Parallel Hammerstein Model
by: R. Z. Akhmetsafina
Published: (2016-10-01)