Search Results for author:
Found 14 papers, 7 papers with code
On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot Learning
Recent advances in audio-text cross-modal contrastive learning have shown its potential towards zero-shot learning.
Audio Editing with Non-Rigid Text Prompts
We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio.
Unsupervised Improvement of Audio-Text Cross-Modal Representations
In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio.
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement.
Semi-supervised Time Domain Target Speaker Extraction with Attention
In this work, we propose Exformer, a time-domain architecture for target speaker extraction.
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
In this paper, we work on a sound recognition system that continually incorporates new sound classes.
Improved singing voice separation with chromagram-based pitch-aware remixing
Singing voice separation aims to separate music into vocals and accompaniment components.
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients.
Compute and memory efficient universal sound source separation
Recent progress in audio source separation lead by deep learning has enabled many neural network models to provide robust solutions to this fundamental estimation problem.
Ranked #5 on
Speech Separation
on WHAMR!
Semi-Supervised Singing Voice Separation with Noisy Self-Training
Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled data to improve the model's performance.
EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors
Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction.
Robotics
Sudo rm -rf: Efficient Networks for Universal Audio Source Separation
In this paper, we present an efficient neural network for end-to-end general purpose audio source separation.
Ranked #11 on
Speech Separation
on WHAMR!
Two-Step Sound Source Separation: Training on Learned Latent Targets
In the first step we learn a transform (and it's inverse) to a latent space where masking-based separation performance using oracles is optimal.
Ranked #26 on
Speech Separation
on WSJ0-2mix
Continual Learning of New Sound Classes using Generative Replay
We show that by incrementally refining a classifier with generative replay a generator that is 4% of the size of all previous training data matches the performance of refining the classifier keeping 20% of all previous training data.
