activity
20162021
most citedData-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning

20 citations · 27 across the 6 of their papers we have counts for

collaborators

7 papers

eess.AS2021

An evaluation of data augmentation methods for sound scene geotagging

Helen L. Bear, Veronica Morfi, Emmanouil Benetos

Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only des…

eess.AS2019

Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Bhusan Chettri, Daniel Stoller, Veronica Morfi +3

Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach. For robustness, we use both deep neura…

cs.SD2018★ 5 cited

NIPS4Bplus: a richly annotated birdsong audio dataset

Veronica Morfi, Yves Bas, Hanna Pamuła +2

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first rich…

cs.LG2018★ 2 cited

Deep Learning for Audio Transcription on Low-Resource Datasets

Veronica Morfi, Dan Stowell

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events presen…

cs.SD2018★ 20 cited

Data-Efficient Weakly Supervised Learning for Low-Resource Audio Event Detection Using Deep Learning

Veronica Morfi, Dan Stowell

We propose a method to perform audio event detection under the common constraint that only limited training data are available. In training a deep learning system to perform audio…

cs.SD2016

Individual identity in songbirds: signal representations and metric learning for locating the information in complex corvid calls

Dan Stowell, Veronica Morfi, Lisa F. Gill

Bird calls range from simple tones to rich dynamic multi-harmonic structures. The more complex calls are very poorly understood at present, such as those of the scientifically impo…