Search Results for author:
Found 11 papers, 5 papers with code
VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge
This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022.
Epic-Sounds: A Large-scale Dataset of Actions That Sound
We introduce EPIC-SOUNDS, a large-scale dataset of audio annotations capturing temporal extents and class labels within the audio stream of the egocentric videos.
In search of strong embedding extractors for speaker diarisation
First, the evaluation is not straightforward because the features required for better performance differ between speaker verification and diarisation.
With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
We capitalise on the action's temporal context and propose a method that learns to attend to surrounding actions in order to improve recognition performance.
VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020.
Augmentation adversarial training for self-supervised speaker recognition
Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general.
Spot the conversation: speaker diarisation in the wild
Finally, we use this pipeline to create a large-scale diarisation dataset called VoxConverse, collected from 'in the wild' videos, which we will release publicly to the research community.
Modeling Musical Onset Probabilities via Neural Distribution Learning
Musical onset detection can be formulated as a time-to-event (TTE) or time-since-event (TSE) prediction task by defining music as a sequence of onset events.
Delving into VoxCeleb: environment invariant speaker recognition
Research in speaker recognition has recently seen significant progress due to the application of neural network models and the availability of new large-scale datasets.
Phase-aware Speech Enhancement with Deep Complex U-Net
Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.
Neural Distribution Learning for generalized time-to-event prediction
Predicting the time to the next event is an important task in various domains.
