Audio Engineering Lab

Some of our current and recent research projects include;

PhD Study - interested in joining the team? We are currently accepting PhD applications.

Aims

We develop intelligent recording techniques, for use by audio editors, mixers and sound engineers, which speed up the recording process, minimise preparation for live performance, and enable easy preparation and transmission of high resolution audio.

Advances in signal processing, machine learning and adaptive systems, have rarely been applied to the professional audio market. This is partly because most digital signal processing applications in these areas have remained focused on replicating or improving those techniques which could be applied in the analogue domain. And until recently, mixing consoles and audio workstations did not have the computing power to allow the introduction of multi-input, multi-output processing tools. Thus audio effects have traditionally been limited to those which operate only on single or stereo channels. There is now an opportunity to develop advanced audio effects which analyse all input channels in order to produce the ideal mix.

Audio engineering for live sound production represents a field with strong potential for improvement and automation. Much of the effort of a sound engineer in preparation for a live performance is consumed by tedious, repetitive tasks. Levels must be set to avoid feedback, input channels must be panned to stereo or surround sound, equalisation, normalisation and compression must be applied to each channel, and all equipment must be tested along with establishing an optimal choice of microphone placement. Only after these tasks have been performed, if time and resources permit, may the sound engineer refine these choices to produce an aesthetically pleasing mix which best captures the intended sound. There is a need for tools which minimise sound-checks by automating complex but non-artistic tasks, establish recommended settings based on the input signals and acoustics, and identifying and avoid issues such as acoustic feedback and microphone crossover.

We develop and test techniques to convert audio mixes between formats. We are working to devise methods to automatically create a surround sound mix, which minimise the masking of sources and places sources in positions that are most subjectively pleasing to the listener.

We investigate methodologies of audio editing used by professional sound engineers, in order to better establish best practices and specify the metadata which will be used to enable automation of audio editing.

The benefits of these techniques are demonstrated by developing, evaluating and deploying prototype systems for intelligent recording and sound reproduction. Personnel

Members

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Alexander Williams

PhD Student

User-driven deep music generation in digital audio workstations

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Brendan O'Connor

PhD Student

Singing Voice Attribute Transformation

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Chin-Yun Yu

PhD Student

Neural Audio Synthesis with Expressiveness Control

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Christian Steinmetz

PhD Student

End-to-end generative modeling of multitrack mixing with non-parallel data and adversarial networks

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Christopher Mitcheltree

PhD Student

Representation Learning for Audio Production Style and Modulations

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David Südholt

PhD Student

Machine Learning of Physical Models for Voice Synthesis

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Gary Bromham

PhD Student

The role of nostalga in music production

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Prof. Joshua D Reiss

Professor of Audio Engineering

sound engineering, intelligent audio production, sound synthesis, audio effects, automatic mixing

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Marco Comunità

PhD Student

Machine learning applied to sound synthesis models

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Nelly Garcia

PhD Student

An investigation evaluating realism in sound design

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Soumya Sai Vanka

PhD Student

Music Production Style Transfer and Mix Similarity

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Xavier Riley

PhD Student

Pitch tracking for music applications - beyond 99% accuracy

School of Electronic Engineering and Computer Science
Queen Mary University of London
Mile End Road
London
E1 4NS
United Kingdom

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