6 citations · 12 across the 6 of their papers we have counts for
8 papers · 1 filter
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
Pablo A. Alvarado, Mauricio A. Álvarez, Dan Stowell
Gaussian process (GP) audio source separation is a time-domain approach that circumvents the inherent phase approximation issue of spectrogram based methods. Furthermore, through i…
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…
Unifying Probabilistic Models for Time-Frequency Analysis
William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss +2
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts. They adapt to the incoming signal, quantify uncertaint…
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…
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…
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…