7 research outputs found
Extended playing techniques: The next milestone in musical instrument recognition
The expressive variability in producing a musical note conveys information
essential to the modeling of orchestration and style. As such, it plays a
crucial role in computer-assisted browsing of massive digital music corpora.
Yet, although the automatic recognition of a musical instrument from the
recording of a single "ordinary" note is considered a solved problem, automatic
identification of instrumental playing technique (IPT) remains largely
underdeveloped. We benchmark machine listening systems for query-by-example
browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets
of instrument, mute, and technique. We identify and discuss three necessary
conditions for significantly outperforming the traditional mel-frequency
cepstral coefficient (MFCC) baseline: the addition of second-order scattering
coefficients to account for amplitude modulation, the incorporation of
long-range temporal dependencies, and metric learning using large-margin
nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the
Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for
instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition
(baseline at 44.5%). We interpret this gain through a qualitative assessment of
practical usability and visualization using nonlinear dimensionality reduction.Comment: 10 pages, 9 figures. The source code to reproduce the experiments of
this paper is made available at:
https://www.github.com/mathieulagrange/dlfm201
Design revolutions: IASDR 2019 Conference Proceedings. Volume 1: Change, Voices, Open
In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 1 includes papers from Change, Voices and Open tracks of the conference
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Synthesizing developmental trajectories
<div><p>Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in <i>Drosophila</i>. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics.</p></div