Published in ICASSP24 SASB, 2024
An evaluation of existing large scale speech models for the downstream task of few-shot audio classification
Recommended citation:
Published in InterSpeech23, 2023
This paper proposes a new multi-task learning framework to take advantage of both equivariant and invariant transformation features.
Recommended citation: Heggan, Calum, et al. "MT-SLVR: Multi-Task Self-Supervised Learning for Transformation In (Variant) Representations." arXiv preprint arXiv:2305.17191 (2023).
Published in ICANN22, 2022
This paper establishes a novel few-shot audio classification benchmark
Recommended citation: Heggan, C., Budgett, S., Hospedales, T., Yaghoobi, M. (2022). MetaAudio: A Few-Shot Audio Classification Benchmark. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_19