19,372 research outputs found
Towards musical interaction : 'Schismatics' for e-violin and computer.
This paper discusses the evolution of the Max/MSP
patch used in schismatics (2007, rev. 2010) for electric
violin (Violectra) and computer, by composer Sam
Hayden in collaboration with violinist Mieko Kanno.
schismatics involves a standard performance paradigm
of a fixed notated part for the e-violin with sonically unfixed
live computer processing. Hayden was unsatisfied
with the early version of the piece: the use of attack
detection on the live e-violin playing to trigger stochastic
processes led to an essentially reactive behaviour in the
computer, resulting in a somewhat predictable one-toone
sonic relationship between them. It demonstrated
little internal relationship between the two beyond an
initial e-violin ‘action’ causing a computer ‘event’. The
revisions in 2010, enabled by an AHRC Practice-Led
research award, aimed to achieve 1) a more interactive
performance situation and 2) a subtler and more
‘musical’ relationship between live and processed
sounds. This was realised through the introduction of
sound analysis objects, in particular machine listening
and learning techniques developed by Nick Collins. One
aspect of the programming was the mapping of analysis
data to synthesis parameters, enabling the computer
transformations of the e-violin to be directly related to
Kanno’s interpretation of the piece in performance
Robust sound event detection in bioacoustic sensor networks
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019;
revised August 2019; published October 201
Delayed Decision-making in Real-time Beatbox Percussion Classification
This is an electronic version of an article published in Journal of New Music Research, 39(3), 203-213, 2010. doi:10.1080/09298215.2010.512979. Journal of New Music Research is available online at: www.tandfonline.com/openurl?genre=article&issn=1744-5027&volume=39&issue=3&spage=20
Self-karaoke patterns: an interactive audio-visual system for handsfree live algorithm performance
Self-karaoke Patterns, is an audiovisual study for improvised cello and live algorithms. The work is motivated in part by addressing the practical needs of the performer in ‘handsfree’ live algorithm contexts and in part an aesthetic concern with resolving the tension between conceptual dedication to autonomous algorithms and musical dedication to coherent performance. The elected approach is inspired by recent work investing the role of ‘shape’ in musical performance
Software agents in music and sound art research/creative work: Current state and a possible direction
Composers, musicians and computer scientists have begun to use software-based agents to create music and sound art in both linear and non-linear (non-predetermined form and/or content) idioms, with some robust approaches now drawing on various disciplines. This paper surveys recent work: agent technology is first introduced, a theoretical framework for its use in creating music/sound art works put forward, and an overview of common approaches then given. Identifying areas of neglect in recent research, a possible direction for further work is then briefly explored. Finally, a vision for a new hybrid model that integrates non-linear, generative, conversational and affective perspectives on interactivity is proposed
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