Hearing from Within a Sound: A Series of Techniques for Deconstructing and Spatialising Timbre

We present a series of compositional techniques for deconstructing and spatialsing timbre in an immersive audio environment. These techniques aim to engulf a spectator within a given abstract timbre, by highlighting said timbre’s distinct spectral and gestural characteristics through our approach to sound spatialisation. We have designed these techniques using both additive synthesis, and time-frequency analysis and resynthesis, building upon analytical methods such as the discrete Fourier transform and the joint time-frequency scattering transform. These spatialisation techniques can be used to deconstruct a sound into subsets of spectral and gestural information, which can then be independently positioned in unique locations within an immersive audio environment. We here survey and evaluate how perceptibly cohesive and aesthetically nuanced a timbre remains after deconstruction and spatialisation, when applied in both live performance and studio production contexts. In accordance with their varying design, each spatialisation technique engenders a unique aesthetic experience, affording a listener various means through which to hear from within a sound

Perceptual musical similarity metric learning with graph neural networks

Sound retrieval for assisted music composition depends on evaluating similarity between musical instrument sounds, which is partly influenced by playing techniques. Previous methods utilizing Euclidean nearest neighbours over acoustic features show some limitations in retrieving sounds sharing equivalent timbral properties, but potentially generated using a different instrument, playing technique, pitch or dynamic. In this paper, we present a metric learning system designed to approximate human similarity judgments between extended musical playing techniques using graph neural networks. Such structure is a natural candidate for solving similarity retrieval tasks, yet have seen little application in modelling perceptual music similarity. We optimize a Graph Convolutional Network (GCN) over acoustic features via a proxy metric learning loss to learn embeddings that reflect perceptual similarities. Specifically, we construct the graph's adjacency matrix from the acoustic data manifold with an example-wise adaptive k-nearest neighbourhood graph: Adaptive Neighbourhood Graph Neural Network (AN-GNN). Our approach achieves 96.4% retrieval accuracy compared to 38.5% with a Euclidean metric and 86.0% with a multilayer perceptron (MLP), while effectively considering retrievals from distinct playing techniques to the query example

Bayesian inference of population expansions in domestic bovines

The past population dynamics of four domestic and one wild species of bovine were estimated using Bayesian skyline plots, a coalescent Markov chain Monte Carlo method that does not require an assumed parametric model of demographic history. Four domestic species share a recent rapid population expansion not visible in the wild African buffalo (Syncerus caffer). The estimated timings of the expansions are consistent with the archaeological records of domestication

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

A computational model of ureteral peristalsis and an investigation into ureteral reflux.

The aim of this study is to create a computational model of the human ureteral system that accurately replicates the peristaltic movement of the ureter for a variety of physiological and pathological functions. The objectives of this research are met using our in-house fluid-structural dynamics code (CgLes-Y code). A realistic peristaltic motion of the ureter is modelled using a novel piecewise linear force model. The urodynamic responses are investigated under two conditions of a healthy and a depressed contraction force. A ureteral pressure during the contraction shows a very good agreement with corresponding clinical data. The results also show a dependency of the wall shear stresses on the contraction velocity and it confirms the presence of a high shear stress at the proximal part of the ureter. Additionally, it is shown that an inefficient lumen contraction can increase the possibility of a continuous reflux during the propagation of peristalsis

DMRN+17: Digital Music Research Network One-day Workshop 2022

DMRN+17: Digital Music Research Network One-day Workshop 2022 Queen Mary University of London - Tuesday 20th December 2022. The Digital Music Research Network (DMRN) aims to promote research in the area of Digital Music, by bringing together researchers from UK and overseas universities and industry for its annual workshop. The workshop will include invited and contributed talks and posters. The workshop will be an ideal opportunity for networking with other people working in the area. Keynote speakers: Sander Dieleman Tittle: On generative modelling and iterative refinement. Bio: Sander Dieleman is a Research Scientist at DeepMind in London, UK, where he has worked on the development of AlphaGo and WaveNet. He obtained his PhD from Ghent University in 2016, where he conducted research on feature learning and deep learning techniques for learning hierarchical representations of musical audio signals. His current research interests include representation learning and generative modelling of perceptual signals such as speech, music and visual data. DMRN+17 is sponsored by The UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM); a leading PhD research programme aimed at the Music/Audio Technology and Creative Industries, based at Queen Mary University of London