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‘MetaAudio: A Few-Shot Classification Benchmark’ was released in early April. It contains a variety of benchmark results for researchers to beat in the future. This blog aims to be a more easily digestible breakdown of the work. All of the code for MetaAudio can be found here
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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
Abstract Meta-learning, or learning to learn, has proven to be a popular framework in dealing with few shot problems in recent times. Most of the work using this family of techniques focuses on the image domain, leaving many other interesting data modalities and settings largely unexplored. This project looks at expanding this scope of the available research by considering both time series data and the task of anomaly detection. In the first year of the PhD, work has largely been devoted to time series, focusing specifically on few-shot acoustic classification and its standardisation within literature.
Abstract To date, the majority of the breakthroughs seen in machine learning have been in domains or settings where there was an abundance of data, either real or simulated. In contrast, the capability for humans to quickly recognise and discriminate between types of phenomena, for example in visual or acoustic settings, remains unmatched. Meta-learning, or learning to learn, has proven to be a popular and successful framework in dealing with these kind of few-shot problems. In this presentation I will give some general context to how these frameworks are created and used as well as some details on the different algorithms that have been a staple of the field.
Abstract Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification. This work aims to alleviate this reliance on image-based benchmarks by offering the first comprehensive, public and fully reproducible audio based alternative, covering a variety of sound domains and experimental settings. We compare the few-shot classification performance of a variety of techniques on seven audio datasets (spanning environmental sounds to human-speech). Extending this, we carry out in-depth analyses of joint training (where all datasets are used during training) and cross-dataset adaptation protocols, establishing the possibility of a generalised audio few-shot classification algorithm. Our experimentation shows gradient-based meta-learning methods such as MAML and Meta-Curvature consistently outperform both metric and baseline methods. We also demonstrate that the joint training routine helps overall generalisation for the environmental sound databases included, as well as being a somewhat-effective method of tackling the cross-dataset/domain setting.
Private, Online & In-person, 2019
Private tuition for both primary and secondary students. For primary students I taught both math and science, this also included the preparation of lesson plans and content. For secondary students I tutored both National 5 and Higher mathematics.
Undergraduate course, University of Edinburgh, Informatics, 2021
Aided in the marking of the 2021 Intro to Computer Vision (IVC) course. Used the Gradescope technology.
Undergraduate course, University of Edinburgh, Engineering, 2022
My primary tutoring responsibilities included hosting seminars and tutorials, starting discussions around professional and ethical values in Engineering. Two pieces of written coursework (essay style) were also mandatory for students to complete over the duration of the course. I was involved in the marking of both of these pieces of work.