65 citations · 96 across the 7 of their papers we have counts for
10 papers
Beyond task performance: Decoding bioacoustic embeddings with speech features
Ines Nolasco, Jules Cauzinille, Marius Miron +8
Pretrained audio embeddings are standard in bioacoustics, yet little is known about which acoustic features these models encode, nor which are useful for a given task. This hinders…
Embryonic Exposure to VPA Influences Chick Vocalisations: A Computational Study
Antonella M. C. Torrisi, Inês Nolasco, Paola Sgadò +2
In young animals like poultry chicks (Gallus gallus), vocalisations convey information about affective and behavioural states. Traditional approaches to vocalisation analysis, rely…
Acoustic identification of individual animals with hierarchical contrastive learning
Ines Nolasco, Ilyass Moummad, Dan Stowell +1
Acoustic identification of individual animals (AIID) is closely related to audio-based species classification but requires a finer level of detail to distinguish between individual…
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event Detection
Jinhua Liang, Ines Nolasco, Burooj Ghani +3
Detecting the presence of animal vocalisations in nature is essential to study animal populations and their behaviors. A recent development in the field is the introduction of the…
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…
Learning to detect an animal sound from five examples
Inês Nolasco, Shubhr Singh, Veronica Morfi +14
Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wil…
Rank-based loss for learning hierarchical representations
Ines Nolasco, Dan Stowell
Hierarchical taxonomies are common in many contexts, and they are a very natural structure humans use to organise information. In machine learning, the family of methods that use t…
Towards joint sound scene and polyphonic sound event recognition
Helen L. Bear, Ines Nolasco, Emmanouil Benetos
Acoustic Scene Classification (ASC) and Sound Event Detection (SED) are two separate tasks in the field of computational sound scene analysis. In this work, we present a new datase…
Audio-based identification of beehive states
Inês Nolasco, Alessandro Terenzi, Stefania Cecchi +3
The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeeper…
To bee or not to bee: Investigating machine learning approaches for beehive sound recognition
Inês Nolasco, Emmanouil Benetos
In this work, we aim to explore the potential of machine learning methods to the problem of beehive sound recognition. A major contribution of this work is the creation and release…