114 citations · 283 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…