MLLab at Interspeech 2021

The Machine Listening Lab will be participating to the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021), taking place on 30 August – 3 September both online and in Brno, Czech Republic. The following papers will be presented by MLLab members:

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MLLab students and staff to join the Alan Turing Institute

Two MLLab PhD students and three MLLab academics will join the Alan Turing Institute, the UK’s national institute in artificial intelligence and data science, in Autumn 2021.

The following MLLab PhD students will join the Turing as Enrichment students in 2021/22:

  • Lele Liu – Enrichment project: Cross-domain automatic music audio-to-score transcription
  • Ilaria Manco – Enrichment project: Multimodal deep learning for music information retrieval

The Turing’s Enrichment scheme offers students enrolled on a doctoral programme at a UK university an opportunity to boost their research project with a placement at the Turing for up to 12 months.

The following MLLab academics have been appointed Turing Fellows in 2021/22:

Turing Fellows are scholars with proven research excellence in data science, artificial intelligence or a related field whose research would be significantly enhanced through active involvement with the Turing network of universities and partners.

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MLLab at IJCNN 2021

On 18-22 July 2021, MLLab researchers will participate virtually at the IEEE International Joint Conference on Neural Networks (IJCNN 2021), the flagship conference of the IEEE Computational Intelligence Society and the International Neural Network Society.

The following papers authored/co-authored by MLLab members will be presented at IJCNN 2021:

  • MusCaps: Generating Captions for Music Audio
    Ilaria Manco, Emmanouil Benetos, Elio Quinton and Gyorgy Fazekas
    Paper
  • Revisiting the Onsets and Frames Model with Additive Attention
    Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos and Dorien Herremans
    Paper
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MLLab papers at ICASSP 2021

On 6-11 June 2021, several MLLab researchers will participate virtually at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021). ICASSP is the flagship conference of the IEEE’s Signal Processing Society and is the leading conference in the field of signal processing.

As in previous years, the Machine Listening Lab will have a strong presence at the conference, both in terms of numbers and overall impact. The following papers authored/co-authored by MLLab members will be presented (all papers are publicly available until 14th June 2021):

See you all at ICASSP!

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New data challenge: “Few-shot Bioacoustic Event Detection” at DCASE 2021

We’re pleased to announce a new data challenge: “Few-shot Bioacoustic Event Detection“, a new task within the “DCASE 2021” data challenge event.

We challenge YOU to create a system to detect the calls of birds, hyenas, meerkats and more.

This is a “few shot” task, meaning we only ever have a small number of examples of the sound to be detected. This is a great challenge for machine-learning students and researchers: it is not yet solved, and it is great practical utility for scientists and conservationists monitoring animals in the wild.

We are able to launch this task thanks to a great collaboration of people who contributed data from their own projects. These newly-curated datasets are contributed from projects recorded in Germany, USA, Kenya and Poland.

The training and validation datasets are available now to download. You can use them to develop new recognition systems. In June, the test sets will be made available, and participants will submit the results from their systems for official scoring.

Much more information on the Few-shot Bioacoustic Event Detection DCASE 2021 page.


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Call for PhD applications at the Machine Listening Lab

The Machine Listening Lab is welcoming PhD applications for September 2021 entry. Applicants from all nationalities can apply across different funding schemes and PhD programmes. Current PhD funding opportunities for September 2021 entry include:

Applicants are encouraged to contact prospective supervisors before submitting their application – please send an email to your selected supervisors with your CV and draft research proposal.

Suggested PhD topics offered by Machine Listening Lab academics as part of the AIM PhD Programme include:

Music interestingness in the brain

Supervisor: Dr Huy Phan
in collaboration with Aarhus University

Measuring interestingness of a song when one is listening to the song will not only shed some light on individual music perception, allowing personalized music recommendation, but also open possibility of using music songs as a brain stimulus. This project aims to automatically measure interestingness of a music songs in the brain using Ear-EEG.

An Ear-EEG device will be used to measure the brain signal (EEG) in the ear canals when one is listening to a song, which is then assessed by machine learning algorithms (potentially deep neural networks) to map the recorded EEG signal into an interestingness measure. Data collection will be carried out and a cohort of young and healthy subjects will be recruited for this purpose. This data will allow exploring different machine learning algorithms and techniques for interestingness modelling. Personalisation and multi-modal modelling, that combines music information (either raw signals or high-level musical features, e.g. melody, music genre, etc.) and the EEG, will also be investigated. This is a joint project with the Centre for Ear-EEG, Aarhus University, and the candidate is expected to work with academics in both C4DM and the Centre for Ear-EEG.

Meta-learning for music data

Supervisor: Dr Emmanouil Benetos

Meta-learning, or “learning to learn”, is an emerging area in the broader field of machine learning. Contrary to conventional machine learning approaches where a particular task is solved using a fixed learning algorithm, the main aim of meta-learning is to learn and improve the learning algorithm itself, so that it can absorb information from one task and generalise across unseen tasks. Meta-learning has various uses in machine learning applications, for example in cases where large datasets are unavailable or when we would like to rapidly learn something about a new task without training our model from scratch. It is also closely related to other emerging machine learning concepts, such as multi-task learning, transfer learning, few-shot learning, and self-supervised learning amongst others. While meta-learning has seen a dramatic rise in research interest in recent years, its principles have seen limited adoption in the intersection of music and AI research.

https://www.aim.qmul.ac.uk/This PhD project will investigate methods for meta-learning applied to music data, such as audio recordings or music scores. The successful candidate will investigate, propose and develop novel machine learning methods and software tools for meta-learning, and will apply them to address tasks related to music and audio data analysis. This will result in methods that can rapidly learn from limited music data, or on methods that can learn from one task and generalise to other unseen tasks related to music and audio data analysis.

Suggested PhD topics for studentships in Computer Science or Electronic Engineering programmes include:

Scalable audio event detection and localisation for domestic acoustic monitoring

Supervisor: Dr Huy Phan

Audio event detection and localisation, which is a highly active research topic, entangles the “what” and “where” questions about occurring sound events. It would enable a wide range of novel applications, particularly domestic acoustic monitoring for healthcare. In this application, it is the case that the target acoustic environments are often different from house to house, causing reverberation mismatch particularly when a system is deployed in a totally new environment. This aspect remains uncharted in the current methods proposed for audio event detection and localisation, hindering scalable deployment and robustness of the system. This project aims to evaluate the robustness of the state-of-the-art methods and propose new machine-learning (potentially deep-learning) and inference methods to address this limitation. Furthermore, apart from being robust against environmental mismatch, such a scalable system should be self-adaptive to resources available of a target device (e.g. IoT devices and mobile devices), able to detect event of interest as early as possible.


Voice and language analysis for personality disorder detection

Supervisor: Dr Huy Phan

Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) are two major mental health disorders that can seriously affect the life of patients. An early and correct diagnosis of these diseases is of paramount importance for an early intervention and treatment. This project aims to develop new methods to recognise these mental health disorders. We have interviewed and collected a voice database from a significant number of participants (both healthy and with disease status), the interviews were also transcripted. This database allows to explore different machine learning, particularly deep learning, methods to analyse the bimodal data (voice and language) for disease recognition. Another important aspect of this project is that we are not only interested in recognition but also in identifying acoustic and linguistic markers that are relevant to, and hopefully underpin, the diseases.

Multi-task learning for music information retrieval

Supervisor: Dr Emmanouil Benetos

Music signals and music representations incorporate and express several concepts: pitches, onsets/offsets, chords, beats, instrument identities, sound sources, and key to name but a few. In the field of music information retrieval, methods for automatically extracting information from audio focus only on isolated concepts and tasks, thus ignoring the interdependencies and connections between musical concepts. Recent advances in machine and deep learning have showed the potential of multi-task learning (MTL), where multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This research project will investigate methods for multi-task learning for music information retrieval. The successful candidate will investigate, propose and develop novel machine learning methods and software tools for jointly estimating multiple musical concepts from complex audio signals. This will result in improved learning efficiency and prediction accuracy when compared to task-specific models, and will help gain a deeper understanding on the connections between musical concepts.

Sound recognition in everyday environments

Supervisor: Dr Emmanouil Benetos

The emerging field of sound scene analysis refers to the development of software systems for automatically recognising everyday sounds and the environment/context of a recording. Applications of sound scene analysis include smart homes, urban planning, audio-based security/surveillance, indexing of sound archives, and acoustic ecology. This project will focus on recognizing sounds from everyday environments. You will carry out research and develop computational methods suitable for detecting overlapping sound events from noisy and complex audio, recorded in urban environments. In this project you will be based in the Machine Listening Lab in the Centre for Digital Music, developing new methods and software tools based on signal processing and machine learning theory.

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Honorary Lectureship for Helen Bear

We’re pleased to announce that Helen Bear (Yogi), who has been working with us since 2018, has been awarded a QMUL Honorary Lectureship for 3 years.

Yogi says:

“I’m delighted to continue being a part of the team in C4DM. Complementary to my work in industry, at QMUL I am excited to continue my work in applied AI for visual and audio domains. Most recently I have been working in environmental sound scene analysis for multiple tasks, such as audio geotagging. But additionally I have been creating partnerships across QM including clinicians at the St Barts NHS trust to use AI to support healthcare and patients. “

To learn more about Yogi and her work, you can read this recent interview in Wonk Magazine.

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Welcoming new Lecturer: Huy Phan

We’re pleased to welcome new Lecturer Huy Phan to our group!

Huy is a Lecturer in AI, and joined the C4DM in April this year. His interests are a great match to the Machine Listening Lab, and we look forward to working together (remotely and in person!). Huy says:

Photo: Huy Phan

“I am a Lecturer in AI at C4DM. Before joining QMUL, I was a postdoctoral research assistant at the University of Oxford and a lecturer at the University of Kent. I received PhD degree from the University of Lübeck, Germany. I am interested in applying machine learning to temporal signal analysis and processing (e.g. audio, EEG).

“At C4DM, I hope to join force with colleagues and students to make contribution to multi-view, multi-task, privacy-preserving, and non-iid generalisation perspectives of machine learning algorithms. I will focus on applications like audio event detection and localisation, audio scene classification, speech enhancement, and healthcare.”

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New journal papers from the Machine Listening Lab

The Machine Listening Lab has recently had great success towards publishing journal papers related to the lab’s research priorities on developing new machine learning and signal processing methodologies for audio and timeseries analysis. Our new work ranges from new methods for speaker anti-spoofing, to visibility graphs for large-scale time series analysis, and on new evaluation methodologies for music prediction and transcription.

The list of our recently accepted and published journal papers can be found below; many of them are freely available or have links to preprints so you can read them already:

Posted in Publications

MLLab papers at ICASSP 2020

On 4-8 May 2020, several MLLab researchers will participate virtually at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). ICASSP is the flagship conference of the IEEE’s Signal Processing Society and is the leading conference in the field of signal processing. For this year, ICASSP is organised as a virtual conference offering free registration to attendees.

As in previous years, the Machine Listening Lab will have a strong presence at the conference, both in terms of numbers and overall impact. The following papers authored/co-authored by MLLab members will be presented:

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