no code implementations • 4 Apr 2024 • Rita Pucci, Vincent J. Kalkman, Dan Stowell
The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application?
1 code implementation • 27 Mar 2024 • Jinhua Liang, Ines Nolasco, Burooj Ghani, Huy Phan, Emmanouil Benetos, Dan Stowell
A recent development in the field is the introduction of the task known as few-shot bioacoustic sound event detection, which aims to train a versatile animal sound detector using only a small set of audio samples.
no code implementations • 14 Dec 2023 • Drew Priebe, Burooj Ghani, Dan Stowell
Our findings revealed that the distilled models exhibited comparable performance to the EcoVAD teacher model, indicating a promising approach to overcoming computational barriers for real-time ecological monitoring.
1 code implementation • 8 Nov 2023 • Giulio Tosato, Abdelrahman Shehata, Joshua Janssen, Kees Kamp, Pramatya Jati, Dan Stowell
This study investigates the potential of automated deep learning to enhance the accuracy and efficiency of multi-class classification of bird vocalizations, compared against traditional manually-designed deep learning models.
no code implementations • 2 Nov 2023 • Shubhr Singh, Christian J. Steinmetz, Emmanouil Benetos, Huy Phan, Dan Stowell
Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging.
no code implementations • 20 Jul 2023 • Rita Pucci, Vincent J. Kalkman, Dan Stowell
Although we observe high performances with all three families of models, our analysis shows that the hybrid model outperforms the fully convolutional-base and fully transformer-base models on accuracy performance and the fully transformer-base model outperforms the others on inference speed and, these prove the transformer to be robust to the shortage of samples and to be faster at inference time.
1 code implementation • 15 Jun 2023 • Ines Nolasco, Burooj Ghani, Shubhr Singh, Ester Vidaña-Vila, Helen Whitehead, Emily Grout, Michael Emmerson, Frants Jensen, Ivan Kiskin, Joe Morford, Ariana Strandburg-Peshkin, Lisa Gill, Hanna Pamuła, Vincent Lostanlen, Dan Stowell
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 interest.
1 code implementation • 11 Oct 2021 • Ines Nolasco, Dan Stowell
We show that rank based loss is suitable to learn hierarchical representations of the data.
1 code implementation • 6 Dec 2020 • Marco Comunità, Dan Stowell, Joshua D. Reiss
Despite the popularity of guitar effects, there is very little existing research on classification and parameter estimation of specific plugins or effect units from guitar recordings.
no code implementations • 5 Oct 2020 • Yahya Al Lawati, Jack Kelly, Dan Stowell
The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR).
1 code implementation • 8 May 2019 • Delia Fano Yela, Florian Thalmann, Vincenzo Nicosia, Dan Stowell, Mark Sandler
The empirical evidence suggests the proposed method for computation of visibility graphs offers an on-line computation solution at no additional computation time cost.
Data Structures and Algorithms
1 code implementation • 5 Mar 2019 • Delia Fano Yela, Dan Stowell, Mark Sandler
We present experiments demonstrating the utility of this distance measure for real and synthesised audio data.
Sound Audio and Speech Processing
1 code implementation • 31 Jan 2019 • William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis.
1 code implementation • 6 Nov 2018 • William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin
In audio signal processing, probabilistic time-frequency models have many benefits over their non-probabilistic counterparts.
1 code implementation • 6 Nov 2018 • Veronica Morfi, Yves Bas, Hanna Pamuła, Hervé Glotin, Dan Stowell
Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings.
Sound Digital Libraries Audio and Speech Processing
1 code implementation • 30 Oct 2018 • Pablo A. Alvarado, Mauricio A. Álvarez, Dan Stowell
As a result, source separation GP models have been restricted to the analysis of short audio frames.
no code implementations • 17 Jul 2018 • Veronica Morfi, Dan Stowell
We propose a method to perform audio event detection under the common constraint that only limited training data are available.
no code implementations • 16 Jul 2018 • Dan Stowell, Yannis Stylianou, Mike Wood, Hanna Pamuła, Hervé Glotin
Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health.
Sound Audio and Speech Processing
no code implementations • 10 Jul 2018 • Veronica Morfi, Dan Stowell
In training a deep learning system to perform audio transcription, two practical problems may arise.
no code implementations • 2 Feb 2018 • William J. Wilkinson, Joshua D. Reiss, Dan Stowell
Recent advances in analysis of subband amplitude envelopes of natural sounds have resulted in convincing synthesis, showing subband amplitudes to be a crucial component of perception.
1 code implementation • 19 May 2017 • Pablo A. Alvarado, Dan Stowell
Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording.
no code implementations • 11 Aug 2016 • Dan Stowell, Mike Wood, Yannis Stylianou, Hervé Glotin
Many biological monitoring projects rely on acoustic detection of birds.
Sound
no code implementations • 3 Jun 2016 • Pablo A. Alvarado, Dan Stowell
We present a Bayesian approach for modelling music audio, and content analysis.
no code implementations • 20 Sep 2015 • Dan Stowell, Richard E. Turner
Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical.
no code implementations • 13 Nov 2014 • Daniele Barchiesi, Dimitrios Giannoulis, Dan Stowell, Mark D. Plumbley
We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques.
no code implementations • 26 May 2014 • Dan Stowell, Mark D. Plumbley
Feature learning can be performed at large scale and "unsupervised", meaning it requires no manual data labelling, yet it can improve performance on "supervised" tasks such as classification.