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

DMRN+18: Digital Music Research Network One-day Workshop 2023

DMRN+18: Digital Music Research Network One-day Workshop 2023 Queen Mary University of London Tuesday 19th December 2023 • Keynote speaker: Stefan Bilbao 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, as well as 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: Stefan Bilbao Tittle: Physics-based Audio: Sound Synthesis and Virtual Acoustics. Abstract: Any acoustically-produced sound produced must be the result of physical laws that describe the dynamics of a given system---always at least partly mechanical, and sometimes with an electronic element as well. One approach to the synthesis of natural acoustic timbres, thus, is through simulation, often referred to in this context as physical modelling, or physics-based audio. In this talk, the principles of physics-based audio, and the various different approaches to simulation are described, followed by a set of examples covering: various musical instrument types; the important related problem of the emulation of room acoustics or “virtual acoustics”; the embedding of instruments in a 3D virtual space; electromechanical effects; and also new modular instrument designs based on physical laws, but without a counterpart in the real world. Some more technical details follow, including the strengths, weaknesses and limitations of such methods, and pointers to some links to data-centred black-box approaches to sound generation and effects processing. The talk concludes with some musical examples and recent work on moving such algorithms to a real-time setting.. Bio: Stefan is a Professor (full) at Reid School of Music, University of Edinburgh, he is the Personal Chair of Acoustics and Audio Signal Processing, Music. He currently works on computational acoustics, for applications in sound synthesis and virtual acoustics. Special topics of interest include: Finite difference time domain methods, distributed nonlinear systems such as strings and plates, architectural acoustics, spatial audio in simulation, multichannel sound synthesis, and hardware and software realizations. More information on: https://www.acoustics.ed.ac.uk/group-members/dr-stefan-bilbao/ DMRN+18 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

Large-Scale Pretrained Model for Self-Supervised Music Audio Representation Learning

Self-supervised learning technique is an under-explored topic for music audio due to the challenge of designing an appropriate training paradigm. We hence propose MAP-MERT, a large-scale music audio pre-trained model for general music understanding. We achieve performance that is comparable to the state-of-the-art pre-trained model Jukebox using less than 2% of parameters

On the effectiveness of speech self-supervised learning for music

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train 12 SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms

DMRN+16: Digital Music Research Network One-day Workshop 2021

DMRN+16: Digital Music Research Network One-day Workshop 2021 Queen Mary University of London Tuesday 21st December 2021 Keynote speakers Keynote 1. Prof. Sophie Scott -Director, Institute of Cognitive Neuroscience, UCL. Title: "Sound on the brain - insights from functional neuroimaging and neuroanatomy" Abstract In this talk I will use functional imaging and models of primate neuroanatomy to explore how sound is processed in the human brain. I will demonstrate that sound is represented cortically in different parallel streams. I will expand this to show how this can impact on the concept of auditory perception, which arguably incorporates multiple kinds of distinct perceptual processes. I will address the roles that subcortical processes play in this, and also the contributions from hemispheric asymmetries. Keynote 2: Prof. Gus Xia - Assistant Professor at NYU Shanghai Title: "Learning interpretable music representations: from human stupidity to artificial intelligence" Abstract Gus has been leading the Music X Lab in developing intelligent systems that help people better compose and learn music. In this talk, he will show us the importance of music representation for both humans and machines, and how to learn better music representations via the design of inductive bias. Once we got interpretable music representations, the potential applications are limitless

Replication-Competent Recombinant Porcine Reproductive and Respiratory Syndrome (PRRS) Viruses Expressing Indicator Proteins and Antiviral Cytokines

Porcine reproductive and respiratory syndrome virus (PRRSV) can subvert early innate immunity, which leads to ineffective antimicrobial responses. Overcoming immune subversion is critical for developing vaccines and other measures to control this devastating swine virus. The overall goal of this work was to enhance innate and adaptive immunity following vaccination through the expression of interferon (IFN) genes by the PRRSV genome. We have constructed a series of recombinant PRRS viruses using an infectious PRRSV cDNA clone (pCMV-P129). Coding regions of exogenous genes, which included Renilla luciferase (Rluc), green and red fluorescent proteins (GFP and DsRed, respectively) and several interferons (IFNs), were constructed and expressed through a unique subgenomic mRNA placed between ORF1b and ORF2 of the PRRSV infectious clone. The constructs, which expressed Rluc, GFP, DsRed, efficiently produced progeny viruses and mimicked the parental virus in both MARC-145 cells and porcine macrophages. In contrast, replication of IFN-expressing viruses was attenuated, similar to the level of replication observed after the addition of exogenous IFN. Furthermore, the IFN expressing viruses inhibited the replication of a second PRRS virus co-transfected or co-infected. Inhibition by the different IFN subtypes corresponded to their anti-PRRSV activity, i.e., IFNω5 ° IFNα1 > IFN-β > IFNδ3. In summary, the indicator-expressing viruses provided an efficient means for real-time monitoring of viral replication thus allowing high‑throughput elucidation of the role of host factors in PRRSV infection. This was shown when they were used to clearly demonstrate the involvement of tumor susceptibility gene 101 (TSG101) in the early stage of PRRSV infection. In addition, replication‑competent IFN-expressing viruses may be good candidates for development of modified live virus (MLV) vaccines, which are capable of reversing subverted innate immune responses and may induce more effective adaptive immunity against PRRSV infection

Differential game theory for versatile physical human-robot interaction

The last decades have seen a surge of robots working in contact with humans. However, until now these contact robots have made little use of the opportunities offered by physical interaction and lack a systematic methodology to produce versatile behaviours. Here, we develop an interactive robot controller able to understand the control strategy of the human user and react optimally to their movements. We demonstrate that combining an observer with a differential game theory controller can induce a stable interaction between the two partners, precisely identify each other’s control law, and allow them to successfully perform the task with minimum effort. Simulations and experiments with human subjects demonstrate these properties and illustrate how this controller can induce different representative interaction strategies

Magnetic Modulation in Mechanical Alloyed Cr1.4fe0.6o3 Oxide

We have synthesized Cr1.4Fe0.6O3 compound through mechanical alloying of Cr2O3 and Fe2O3 powders and subsequent thermal annealing. The XRD spectrum, SEM picture and microanalysis of EDAX spectrum have been used to understand the structural evolution in the alloyed compound. The alloyed samples are matching to rhombohedral structure with R3C space group. The observation of a modulated magnetic order confirmed a systematic diffusion of Fe atoms into the Cr sites of lattice structure. A field induced magnetic behaviour is seen in the field dependence of magnetization data of the annealed samples. The behaviour is significantly different from the mechanical alloyed samples. The experimental results provided the indications of considering the present material as a potential candidate for opto-electronic applications.Comment: 8 figure

Non-solvolytic synthesis of aqueous soluble TiO2 nanoparticles and real-time dynamic measurements of the nanoparticle formation.

Highly aqueously dispersible (soluble) TiO2 nanoparticles are usually synthesized by a solution-based sol-gel (solvolysis/condensation) process, and no direct precipitation of titania has been reported. This paper proposes a new approach to synthesize stable TiO2 nanoparticles by a non-solvolytic method - direct liquid phase precipitation at room temperature. Ligand-capped TiO2 nanoparticles are more readily solubilized compared to uncapped TiO2 nanoparticles, and these capped materials show distinct optical absorbance/emission behaviors. The influence of ligands, way of reactant feeding, and post-treatment on the shape, size, crystalline structure, and surface chemistry of the TiO2 nanoparticles has been thoroughly investigated by the combined use of X-ray diffraction, transmission electron microscopy, UV-visible (UV-vis) spectroscopy, and photoluminescence (PL). It is found that all above variables have significant effects on the size, shape, and dispersivity of the final TiO2 nanoparticles. For the first time, real-time UV-vis spectroscopy and PL are used to dynamically detect the formation and growth of TiO2 nanoparticles in solution. These real-time measurements show that the precipitation process begins to nucleate after an initial inhibition period of about 1 h, thereafter a particle growth occurs and reaches the maximum point after 2 h. The synthesis reaction is essentially completed after 4 h.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are