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20132023
most citedFew-shot bioacoustic event detection at the DCASE 2023 challenge

6 citations · 12 across the 6 of their papers we have counts for

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cs.SD20236 cited

Few-shot bioacoustic event detection at the DCASE 2023 challenge

Ines Nolasco, Burooj Ghani, Shubhr Singh +12

Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of inter…

cs.SD2019

Spectral Visibility Graphs: Application to Similarity of Harmonic Signals

Delia Fano Yela, Dan Stowell, Mark Sandler

Graph theory is emerging as a new source of tools for time series analysis. One promising method is to transform a signal into its visibility graph, a representation which captures…

cs.SD2018

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.SD2018

Automatic acoustic identification of individual animals: Improving generalisation across species and recording conditions

Dan Stowell, Tereza Petrusková, Martin Šálek +1

Many animals emit vocal sounds which, independently from the sounds' function, embed some individually-distinctive signature. Thus the automatic recognition of individuals by sound…

cs.SD2018

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.SD2018

Does k Matter? k-NN Hubness Analysis for Kernel Additive Modelling Vocal Separation

Delia Fano Yela, Dan Stowell, Mark Sandler

Kernel Additive Modelling (KAM) is a framework for source separation aiming to explicitly model inherent properties of sound sources to help with their identification and separatio…