114 citations · 283 across the 14 of their papers we have counts for
15 papers
On Class Separability Pitfalls In Audio-Text Contrastive Zero-Shot Learning
Tiago Tavares, Fabio Ayres, Zhepei Wang +1
Recent advances in audio-text cross-modal contrastive learning have shown its potential towards zero-shot learning. One possibility for this is by projecting item embeddings from p…
Audio Editing with Non-Rigid Text Prompts
Francesco Paissan, Luca Della Libera, Zhepei Wang +3
In this paper, we explore audio-editing with non-rigid text edits. 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
Zhepei Wang, Cem Subakan, Krishna Subramani +4
Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional training approaches that use predefined labe…
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
Zhepei Wang, Ritwik Giri, Devansh Shah +3
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement. This is achieved by i…
Semi-supervised Time Domain Target Speaker Extraction with Attention
Zhepei Wang, Ritwik Giri, Shrikant Venkataramani +5
In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on…
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
Zhepei Wang, Cem Subakan, Xilin Jiang +4
In this paper, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framework where the model can be updated without…
Improved singing voice separation with chromagram-based pitch-aware remixing
Siyuan Yuan, Zhepei Wang, Umut Isik +4
Singing voice separation aims to separate music into vocals and accompaniment components. One of the major constraints for the task is the limited amount of training data with sepa…
Sound Event Detection with Adaptive Frequency Selection
Zhepei Wang, Jonah Casebeer, Adam Clemmitt +2
In this work, we present HIDACT, a novel network architecture for adaptive computation for efficiently recognizing acoustic events. We evaluate the model on a sound event detection…
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
Efthymios Tzinis, Jonah Casebeer, Zhepei Wang +1
We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients. We simulate a r…
Compute and memory efficient universal sound source separation
Efthymios Tzinis, Zhepei Wang, Xilin Jiang +1
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. In this…
Semi-Supervised Singing Voice Separation with Noisy Self-Training
Zhepei Wang, Ritwik Giri, Umut Isik +2
Recent progress in singing voice separation has primarily focused on supervised deep learning methods. However, the scarcity of ground-truth data with clean musical sources has bee…
Sudo rm -rf: Efficient Networks for Universal Audio Source Separation
Efthymios Tzinis, Zhepei Wang, Paris Smaragdis
In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is…
Two-Step Sound Source Separation: Training on Learned Latent Targets
Efthymios Tzinis, Shrikant Venkataramani, Zhepei Wang +2
In this paper, we propose a two-step training procedure for source separation via a deep neural network. In the first step we learn a transform (and it's inverse) to a latent space…
Continual Learning of New Sound Classes using Generative Replay
Zhepei Wang, Cem Subakan, Efthymios Tzinis +2
Continual learning consists in incrementally training a model on a sequence of datasets and testing on the union of all datasets. In this paper, we examine continual learning for t…
Multi-View Networks For Multi-Channel Audio Classification
Jonah Casebeer, Zhepei Wang, Paris Smaragdis
In this paper we introduce the idea of multi-view networks for sound classification with multiple sensors. We show how one can build a multi-channel sound recognition model trained…