Music Information Retrieval
in Live Coding: A
Theoretical Framework
Anna Xamb ´o,∗ Alexander Lerch,†
and Jason Freeman†
∗Music Technology
Department of Music
Norwegian University of Science and
Technology
7491 Trondheim, Norway
†Center for Music Technology
Georgia Institute of Technology
840 McMillan St NW
Atlanta, Georgia 30332 USA
anna.xambo@ntnu.no
{alexander.lerch,
jason.freeman}@gatech.edu
Abstract: Music information retrieval (MIR) has a great potential in musical live coding because it can help the
musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by
means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have
been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction.
This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a
conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored
the three categories in live performance through an application programming interface library written in SuperCollider,
MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and
tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer
perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future
directions of utilizing MIR approaches in the computer music field.
Live coding in music is an improvisation practice
based on generating code in real time by either
writing it directly or using interactive programming
(Brown 2006; Collins et al. 2007; Rohrhuber et al.
2007; Freeman and Troyer 2011). Music information
retrieval (MIR) is an interdisciplinary research field
that targets, generally speaking, the analysis and
retrieval of music-related information, with a focus
on extracting information from audio recordings
(Lerch 2012). Applying MIR techniques to live coding
can contribute to the process of music generation,
both creatively and computationally. A potential
scenario would be to create categorizations of audio
streams and extract information on timbre and
performance content, as well as drive semiautomatic
audio remixing, enabling the live coder to focus on
high-level musical properties and decisions. Another
potential scenario is to be able to model a high-level
music space with certain algorithmic behaviors and
allow the live coder to combine the semi-automatic
retrieval of sounds from both crowdsourced and
personal databases.
This article explores the challenges and opportu-
nities that MIR techniques can offer to live coding
practices. Its main contributions are: a survey re-
view of the state of the art of MIR in live coding;
a categorization of approaches to live coding using
MIR illustrated with examples; an implementation
in SuperCollider of the three approaches that are
demonstrated in test-bed performances; and a dis-
cussion of future directions of real-time computer
music generation based on MIR techniques.
Background
In this section, we overview the state of the art of
live coding environments and MIR related to live
performance applications.
Computer Music Journal, 42:4, pp. 9–25, Winter 2018
doi:10.1162/COMJ a 00484
c(cid:2) 2019 Massachusetts Institute of Technology.
The terms “live coding language” and “live coding
environment,” which support the activity of live
Live Coding Programming Languages
Xamb ´o et al.
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coding in music, will be used interchangeably in
this article. Programming languages that have been
used for live coding include ChucK (Wang 2008),
Extempore (previously Impromptu; Sorensen 2018),
FoxDot (Kirkbride 2016), Overtone (Aaron and
Blackwell 2013), Sonic Pi (Aaron and Blackwell
2013), SuperCollider (McCartney 2002), TidalCycles
(McLean and Wiggins 2010), Max and Pd (Puckette
2002), and Scratch (Resnick et al. 2009). With
the advent and development of JavaScript and Web
Audio, a new generation of numerous Web-based live
coding programming languages has emerged, e.g.,
Gibber (Roberts and Kuchera-Morin 2012). Visual
live coding is also an emerging practice, notably
with Hydra (https://github.com/ojack/hydra) and
Cyril (http://cyrilcode.com/).
Commercial Software Using MIR
for Live Performance
Real-time MIR has been investigated and imple-
mented in mainstream software ranging from: digi-
tal audio workstations (DAWs) such as Ableton Live;
karaoke software such as Karaoki (http://www.pcdj
.com/karaoke-software/karaoki/) and My Voice
Karaoke (http://www.emediamusic.com/karaoke
-software/my-voice-karaoke.html); DJ software such
as Traktor (https://www.native-instruments.com
/en/products/traktor/), Dex 3 (http://www.pcdj.com
/dj-software/dex-3/), and Algoriddim’s Djay Pro
2 (https://www.algoriddim.com/); and song re-
trieval or query-by-humming applications such as
Midomi (https://www.midomi.com/) and Sound-
Hound (https://soundhound.com/). Typically, these
solutions include specialized MIR tasks, such as
pitch tracking for the karaoke software, and beat
and key detection for the DJ software. In these ap-
plications, the MIR tasks are focused on high-level
musical information (as opposed to low-level feature
extraction) and thus inspire this research. There are
several collaborations and conversations between
industry and research looking at real-time MIR
applied to composition, editing, and performance
(Bernardini et al. 2007; Serra et al. 2013). To our
knowledge, there has been little research on the
synergies between MIR in musical live coding and
MIR in commercial software. The present article
aims to help fill that gap.
Real-Time Feature Extraction Tools for Live
Coding Environments
In this section, we present a largely chronological
and functional analysis of existing MIR tools for live
coding and interactive programming to understand
the characteristics and properties of the features
supported, identify the core set of features, and
discuss whether the existing tools satisfy the
requirements of live coders.
Nearly all MIR systems extract a certain interme-
diate feature representation from the input audio and
use this representation for inference—for example,
classification or regression. Although still an open
debate, there is some common ground in the MIR
literature about the categorization of audio features
between low-, mid-, and high-level. In an earlier
publication, the second author referred to low-level
features as instantaneous features, extracted from
small blocks of audio and describing various, mostly
technical properties, such as the spectral shape,
intensity, or a simple characterization of pitched
content (Lerch 2012). Serra et al. (2013) refer to
tonal and temporal descriptors as midlevel fea-
tures. High-level features usually describe semantic
characteristics, e.g., emotion, mood, musical form,
and style (Lerch 2012), or overall musical, cultural,
and psychological characteristics, such as genre,
harmony, mood, rhythm, and tonality (Serra et al.
2013). The distinction between low-, mid-, and
high-level audio features is used here because it
provides a sufficient level of granularity to be useful
to the live coder.
Various audio feature extraction tools for live
coding have existed for some time. In this section,
we present an overview of existing tools for the three
environments ChucK (Wang 2008), Max (Puckette
2002), and SuperCollider (McCartney 2002), because
of their popularity, extensive functionality, and
maturity. The findings and conclusions, however,
are generalizable to other live coding environments.
A illustrative selection of MIR tools for analysis is
listed in Table 1.
10
Computer Music Journal
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Table 1. Selected MIR Tools for Live Coding Environments
Library Type
MIR Tool
Authors
Live Coding
Environment
Included
Third-party
Unit Analyzer (UAna)
fiddle∼,bonk∼ and sigmund∼ objects
Built-in UGens for machine listening
SMIRK
LibXtract
Zsa.Descriptors
analyzer∼ object
Mubu
SCMIR
Freesound quark
ChucK
Wang, Fiebrink, and Cook (2007)
Puckette, Apel, and Zicarelli (1998) Max
SuperCollider Community
Fiebrink, Wang, and Cook (2008)
Jamie Bullock (2007)
Malt and Jourdan (2008)
Tristan Jehan
Schnell et al. (2009)
Nick Collins (2011)
Gerard Roma
SuperCollider
ChucK
Max, SuperCollider
Max
Max
Max
SuperCollider
SuperCollider
Figure 1 shows a timeline of the analyzed MIR
tools from 1990s to present, which provides a histor-
ical and technological perspective of the evolution
of the programming environments and dependent
libraries. As shown in Figure 2, the live coding
environment that includes most audio features is
SuperCollider, and the third-party libraries with
the largest number of features are LibXtract and
Freesound quark, the latter as a wrapper of the
Essentia framework. Figure 3 shows the distribution
of the audio features over six categories (statis-
tical, envelope, intensity, timbre, temporal, and
tonal), which are inspired by Essentia’s organization
(Bogdanov et al. 2013) and Lerch’s (2012) feature
classification.
The timbre audio features are the greatest in
number (43 percent of the total). The most popu-
lar spectral features are spectral centroid, spectral
rolloff, spectral spread, and Mel frequency cepstral
coefficients (MFCCs), followed by the less popular
but still prominent spectral flatness and spectral
flux. The second category with stronger representa-
tion (16 percent of the total) consists of statistical
audio features, with zero-crossing rate the most
frequently implemented. The more commonly sup-
ported intensity audio features are loudness and root
mean square (RMS). In temporal audio features, beat
tracker and onset detection are the most present.
Finally, there is a considerable amount of tonal
audio features (15 percent of the total), where pitch
(F0) is the only feature supported extensively.
Considering these most popular features, it is
noticeable that the majority are low-level (instan-
taneous) features (e.g., RMS or spectral centroid).
These features have been widely used in audio con-
tent analysis—for example, to classify audio signals.
In live coding, they can be helpful to determine
audiovisual changes in real time, yet the amount
of data can be overwhelming and most of the ex-
tracted features are not musically meaningful (e.g.,
how to interpret continuous floating-point values
of MFCCs). Midlevel features, such as onsets and
tonality, can be interpreted more easily and can be
used to characterize rhythmic and tonal properties,
allowing the live coder to make content-driven de-
cisions (e.g., a threshold value can indicate whether
or not to trigger a percussive sound depending on
the onset values).
It is noteworthy that the real-time MIR tools
presented here provide the coder only with few
midlevel features and show a lack of high-level
features (Figure 2). As later discussed in this article,
the use of high-level features (e.g., mood or genre)
requires a longer time scope, and systems extracting
high-level features are often prone to error (e.g.,
see the semantic-gap problem between low-level
descriptors and semantic descriptors described by
Schedl et al. [2014]). This may affect the performance
negatively, compared with the immediacy and ease
of use of instantaneous low-level features. At the
same time, if the technical issues were solved,
high-level information could increase the richness
Xamb ´o et al.
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Figure 1. Timeline of live
coding programming
languages and dependent
MIR tools. Color version of
the figure online at
www.mitpressjournals.org
/doi/suppl/10.1162/COMJ a 00484.
Figure 2. Functional
analysis of MIR tools for
live coding: Distribution of
audio features by number
against low-level and
midlevel audio features.
Color version of the figure
online at
www.mitpressjournals.org
/doi/suppl/10.1162
/COMJ a 00484.
Figure 1
Figure 2
and musicality of the live coding session as the
live coder could make decisions from a musical
perspective of, for example, the overall musical
form.
12
Computer Music Journal
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Figure 3. Functional
analysis of MIR tools for
live coding: Distribution of
audio features by
categories.
Color version of the figure
online at
www.mitpressjournals.org
/doi/suppl/10.1162/COMJ a 00484.
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Conceptual Framework on MIR in Live Coding
The conceptual framework presented here struc-
tures MIR approaches for live coding into three
nonexclusive categories: (1) audio repurposing, by
which we mean the analysis, retrieval, and manipu-
lation of audio clips from a sound or music database;
(2) audio rewiring, by which we mean the real-time
analysis of an audio input signal, which can be
either an external source or the output of the system
itself; and (3) audio remixing, by which we mean a
system supporting musically meaningful remixing
decisions semiautonomously.
For each of the three categories, we present a
prototype. The aim is to provide a conceptual foun-
dation that helps us to discuss future possibilities.
The three prototypes have been developed as three
separate modules that constitute Music Informa-
tion Retrieval for Live Coding (MIRLC, available
at http://github.com/axambo/MIRLC). This is an
application programming interface (API) library
written in SuperCollider that is designed to provide
a musical approach to using MIR techniques in live
coding.
Audio Repurposing
Since the advent of the social Web, sound sharing
and retrieval from online databases (e.g., ccMixter,
Freesound, Looperman) have become increasingly
popular (Font, Roma, and Serra 2017). Sounds are
identified and retrieved by parsing either user-
annotated data (e.g., tags, annotations, descriptions)
or automatically extracted data. Font, Roma, and
Serra identify two types of queries in audio retrieval
based on features: (1) queries by range of a property,
that is, retrieval of sounds that match a given
interval or threshold filtered by one or multiple
audio features and (2) queries by similarity, i.e.,
Xamb ´o et al.
13
Figure 4. Block diagram of
audio repurposing.
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retrieval of similar sounds from a given example. An
instance of the former is giving a range of bpm values
to find specific rhythmic sounds. The latter has been
notably explored for sound-synthesis techniques
such as musical mosaicking (Zils and Pachet 2001),
concatenative sound synthesis (Schwarz 2007), or
mashups (Serra et al. 2013) by retrieving short
sounds similar to a reference. Other useful queries
are by category, e.g., instrument or genre.
Figure 4 illustrates a block diagram of the audio
repurposing approach in live coding, where the
live coder can retrieve sounds from either online
databases or local databases. The retrieved sounds
will be processed and integrated into the perfor-
mance using live coding techniques. For efficient
audio retrieval in quasi–real time, a common prac-
tice is to analyze the sound database in advance and
store its feature representation, thus combining the
time-consuming offline analysis with ad hoc access
to precomputed features and metadata from the
audio clips (Serra et al. 2013). Typically, the offline
analysis uses a feature-extractor program and stores
the information of each audio file as a single vec-
tor containing the aggregated audio features (Font,
Roma, and Serra 2017). The live coding language
deals with low-level operations of real-time audio
processing, such as loading sounds in buffers, which
implies processing blocks of audio samples in real
time.
Examples of Audio Repurposing
Current examples of audio repurposing in live
coding include the retrieval of audio clips, usu-
ally by textual queries from personal or online
databases, as shown in live coding sessions with
14
Computer Music Journal
Gibber (http://youtu.be/uly7DgtfRKI?t=96) or Tidal
(http://youtu.be/FenTeBMkAsQ?t=275), as well as
media clips with live coding YouTube (Lee, Bang,
and Essl 2017), and even any type of input data
(Tsuchiya, Freeman, and Lerner 2016). Similarly, in
gibberwocky multiple audio clips are also used from
a preselected collection of sounds, using a DAW
interface such as Ableton Live connected to Gibber
(Roberts and Wakefield 2017).
Exploring a wider range of scenarios, notable ex-
amples of machine-listening systems can be found,
particularly in art installations, live performances,
and educational tools. These examples, which can
inform future live coding applications, include
BBCut (Collins 2002), the LoopMashVST plugin
(http://youtu.be/SuwVV9zBq5g), Floop (Roma and
Serra 2015), EarSketch (Xamb ´o, Lerch, and Freeman
2016), Freesound Explorer (Font, Roma, and Serra
2017), and APICultor (Ordiales and Bruno 2017),
among others. These systems represent audio clips
by content-based feature analysis, and often retrieve
them in combination with text-based information
(e.g., tags). With the exception of APICultor, they
use a combination of low-level features (e.g., timbral
properties) with midlevel features (e.g., temporal
and tonal properties) for browsing sounds. Floop,
for instance, looks into the beat spectrum to see
how rhythmic a sound is. These systems are highly
constrained to particular-use cases, however. Most
of them are based on audio clips constrained to
particular features, e.g., their rhythm (Floop), beat
(BBCut), or timbre (LoopMashVST). During a live
coding session, it can be desirable to retrieve sound
samples without such constraints. According to
Font, Roma, and Serra (2017), a sound database can
include both short recordings (e.g., acoustic events
or audio fragments) and long recordings (e.g., music,
speech, or environmental sound scenes). Low-level
features, such as MFCCs, can be used in most types
of sounds (e.g., environmental sounds, speech, or
music), whereas higher-level features usually make
assumptions of the input (e.g., detectable onsets and
pitches, identifiable tonality, or minimum length
to detect rhythmic properties). As in APICultor, a
live coding system should have the flexibility of
filtering the results by the choice of multiple audio
features. There are computational challenges related
to feature aggregation that limit the length and
content of this approach, however. Using an online
database with preanalyzed audio features seems to
be a workable approach for live coding, which can
be combined with a preanalyzed local database, as
discussed in previous work (Xamb ´o et al. 2018).
The use of visualization tools (e.g., a two-
dimensional space) for exploring the database
content, as illustrated in EarSketch, Floop, and
Freesound Explorer, can allow for efficient browsing
in a live coding context. As discussed, this approach
requires an offline analysis and each new query
can disrupt the existing sound output if it is not
designed for live performance. An example of an
interactive visual timbral map for sound browsing
suitable for live performance is found in a demo of
SCMIR (http://youtu.be/jxo4StjV0Cg). As shown in
this demo, using visualization techniques to show
browse-and-query processes is a compelling addition
to the live coding practice, yet it broadens the defi-
nition of live coding because visual media is added
to the coding environment, as explored by others
(McLean and Wiggins 2010; Tsuchiya, Freeman, and
Lerner 2016; Lee, Bang, and Essl 2017; Roberts and
Wakefield 2017).
Prototype 1: A Case Study for Exploring
Audio Repurposing
This prototype aims at providing a high-level mu-
sical approach to operate with audio clips in live
coding using MIR, as previously discussed and as-
sessed (Xamb ´o et al. 2018). Figure 5 shows a test-bed
performance using this module. It is designed for
repurposing audio samples from Freesound using Su-
perCollider (online at http://vimeo.com/249968326).
This module is built on top of the Freesound quark
(http://github.com/g-roma/Freesound.sc), a Su-
perCollider client for accessing audio clips from
Freesound through the Freesound API. The benefit
of using the Freesound archive is that it allows
one to browse about 400,000 sounds, either by text
search, content search, or a combination of the
two.
Inspired by the Web interface Freesound Radio
(Roma, Herrera, and Serra 2009), this module
promotes loading sounds in a musically meaningful
Xamb ´o et al.
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Figure 5. The first author
live coding with MIRLC at
the Noiselets
microfestival, 8 January
2017, Barcelona. Color
version of the figure
online at
www.mitpressjournals.org
/doi/suppl/10.1162
/COMJ a 00484.
(Photo by Helena Coll.)
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way. The live coder has a range of options to
retrieve sounds, including mid- and high-level
content-based queries (e.g., duration, bpm, pitch,
key, or scale) and text-based queries (i.e., tags).
Sounds can be retrieved in groups, which facilitates
the creation of conceptually related sound groups
based on similarity, rhythm, or pitch, among
other criteria. Each sound is played in a loop, and
the groups can be played either simultaneously
or sequentially. This provides different levels of
musical granularity. The main functionalities
include asynchronous management of multiple
sounds by a single query or operation; human-like
queries by content, similarity, tag, filter, sound ID,
and sounds chosen at random; and an architecture
to play with the groups of sounds either in sequence
or in parallel. Figure 6 shows an example of the
code.
In this case study, the role of live coding is
to query, browse, and control the audio clips
in real time. Live coding sends out high-level
textual (e.g., tags) and content-based queries (e.g.,
pitch, bpm, key, or scale), the latter based on MIR
information, with the intent of crafting a coherent
sound palette. Using MIR techniques removes the
requirement of individually knowing each sound
to create a homogeneous and coherent sound
pool. The combination of metadata with audio
content analysis provides flexibility and variation
to the performance. It is possible to search for
sounds based on different criteria, such as rhythm,
melody, duration, and harmony. The use of the
similarity descriptor can give musically consistent,
yet unpredictable results, which demonstrates that
defining musical similarity is a nontrivial task
(Lerch 2012). As future work, the MIR processes
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Computer Music Journal
Figure 6. Example of code
for audio repurposing:
Code extract used for the
album H2RI by the first
author (Xamb ´o 2018).
// Instantiation
a = MIRLCRep.new
b = MIRLCRep.new
// Get sounds by content
a.content( 1, ’pitch’, 22000, ’conf’, ’hi’ )
b.content( 1, ’dur’, 0.01, ’conf’, ’hi’ )
( t = Routine( { // Creation of a control structure for sequencing
var delta;
loop {
delta = rrand( 2, 10 ); // Generate a random number between 2 and 10
if ( [ false, true ].choose, // Choose with equal chance the value of false or true
{ a.similar }, // If true, get a similar sound from first sound in group a
{ b.similar } // If false, get a similar sound from first sound in group b
);
delta.yield; // Amount of time in seconds until the routine should execute again
}
} ); )
t.play // Play the routine defined above
( r = Routine( { // Creation of another control structure for sequencing
var delta;
loop {
// Generate a random number between 0.0005 and 0.3
delta = rrand( 0.05, 3 ) * rrand( 0.01, 0.1 );
if ( [ false, true ].choose,
{ b.sequence }, // if true: play sounds of group b in sequence
{ b.parallel } // if false: play sounds of group b in parallel
);
delta.yield;
}
} ); )
r.play
that are taking place could be made more visible to
both the audience and the live coder using textual
feedback, following the notion of “showing the
screen” in live coding. Next steps of interest include
the use of content-based features at even higher
levels (e.g., mood or genre) to be combined with
text-based queries. In the current version, the use of
tags has been an effective workaround (e.g., “happy,”
“sad,” “angry,” “excited,” “techno,” “drumbeat,”
or “dancehall”).
Audio Rewiring
The use of an audio stream as an input is a common
practice in interactive computer music and machine
listening (Chadabe 1984; Rowe 1993, 2001). The
advent of sufficiently fast computers and suitable
software has made feature analysis of the audio input
signal possible in real time. Mapping the analysis
results to sound processing parameters opens a
range of creative possibilities for both studio and
Xamb ´o et al.
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Figure 7. Block diagram of
audio rewiring.
2016). Similarly, there are sound installations
with feedback and real-time analysis, such as
Andrea Valle’s Rumentario Autoedule (online at
vimeo.com/37148011) and Agostino Di Scipio’s
Audible Ecosystems (both installations described by
Sanfilippo and Valle 2013). These examples extract
numerous low-level features (e.g., spectral features)
and a small subset of midlevel features (e.g., tonal
and temporal characteristics). There are, however,
constraints that can limit their usability. In BBCut2,
for example, the audio has to be recorded before one
can apply effects, which adds a delay that is hardly
ideal in a real-time scenario. Similarly, in the Beat-
boxing classifier, even with workarounds in place,
latency cannot easily be avoided and remains an
issue for real-time performance. This indicates the
importance of considering these constraints when
designing an algorithmic or compositional system
for performance. The feedback component of audio
rewiring adds risk of failure to the performance and
at the same time it can bring interesting results.
Audio feedback can potentially make the overall
system unstable; however, this instability can also
be used artistically and can be creatively incorpo-
rated into a performance (see, for instance, work by
artists such as Sonic Arts Union, Loud Objects, and
Gordon Mumma).
Prototype 2: A Case Study for Exploring
Audio Rewiring
This prototype aims at providing a high-level
musical approach to operate with incoming audio
(e.g., acoustic instrument or voice) in live coding
using MIR. It is designed for rewiring an audio
input signal as either a control signal or audio signal
using MIR techniques in SuperCollider (see example
online at http://vimeo.com/249997271). An early
version of the prototype has been used in the piece
Beacon (Weisling and Xamb ´o 2018), a collaborative
audiovisual work between a visual artist and an
electronic musician, which has been performed
internationally (Lee et al. 2018). The visual artist
works with the Distaff system (see Figure 8), a
customized Technics turntable (see Weisling 2017).
The sound of the performer’s fingers interacting
with the turntable (e.g., scratching or tipping) as
live performance, with prominent examples such as
auto-tune or intelligent harmonizers. Furthermore,
the audio output signal can be fed back to the
input of the system to create a feedback system, a
practice that dates back decades. Audio feedback
has been extensively used in computer music, either
as analog audio feedback, digital audio feedback, or
both (Sanfilippo and Valle 2013).
Figure 7 shows the block diagram of the audio
rewiring approach, in which the live coder receives
an incoming audio stream (e.g., microphone, line
in, or system output). The real-time analysis of this
audio signal is used to define control or audio signals
of the system. The live coding language processes
the incoming audio in real time using buffers.
Examples of Audio Rewiring
There are notable examples of live coding systems
making use of this paradigm, including the BBCut2
library (Collins 2006), the Beatboxing classifier
(Stowell and Plumbley 2010), the Algoravethmic
remix system (Collins 2015), and Sound Choreogra-
phy <> Body Code (McLean and Sicchio 2014, see
also the example at http://vimeo.com/62323808).
Beyond live coding, some examples combine hard-
ware and real-time audio analysis, for instance,
the Machine Listening Eurorack module (Latina
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Figure 8. Closeup of Anna
Weisling’s Distaff system,
shown at the Conference
on New Interfaces for
Musical Expression 2017,
Stengade, Copenhagen.
Color version of the figure
online at
www.mitpressjournals.org
/doi/suppl/10.1162
/COMJ a 00484. (Photo by
Jimmi Brandt Fotos.)
well as the inner mechanical sounds of the device
produced from these interactions, are captured with
a lavalier microphone located inside the wooden box
that contains the mechanical parts of the original
turntable. Audio features extracted from the audio
input signal either control effects applied to the
audio signal or parameters of other audio signals. In
this prototype, we explore unidirectional control (as
opposed to a feedback loop).
An example of the code is shown in Figure 9.
The use of an audio input signal with different
roles throughout the piece (e.g., audio signal or
control signal) provides versatility and a wide range
of variation. Live coding is used to change the role
of the audio input signal. Feature analysis of the
audio signal is applied to either control a sound
generator (e.g., an estimated beat from the source
system triggers a kick drum sound) or to modulate
an effect parameter (e.g., an estimated onset from
the source system modulates a multigrain effect).
The rhythmical nature of the audio source shapes
the mappings, thus the mapping design can be
reconsidered when using a sound source that is less
percussive, for example, a voice.
The prototype presented here uncovered some
conceptual drawbacks. Although a high level of
synchronicity was noticed, the audience was aware
of neither the role of live coding for generating sound
nor the general system setup involving audio and
control signals from the turntable. Furthermore,
in collaborations between a live coder and a visual
artist, there can be a conflict of how to make both
processes visible when visuals are so prominent.
Previous research has explored the extent to which
both approaches (opacity and transparency) are used
equally and combined in audiovisual performance
(Weisling et al. 2018). The decision of creating a
more immersive environment by not focusing the
audience’s attention on a screen with code extends
the spectrum of live coding practices. Given the
nature of the piece, a sound palette and algorithmic
rules had to be defined ahead for each section,
where improvisation was determined by the score
of the piece. A future direction could be designing
an environment with fewer constraints by adding
more flexibility to the mappings. This would allow a
greater number of spontaneous decisions to be made
in real time, similar to UrSound (Essl 2010) and
Gibber (Roberts et al. 2014). An interesting future
challenge would be the use of higher-level audio
features (e.g., mood or genre) to make decisions in
real time. This would introduce some latency for
accurate classification of events, so that strategies
for working around processing delays would be
required (for further discussison, see Stowell and
Plumbley 2010).
Audio Remixing
There is a long tradition of network music in
computer music (Weinberg 2005; Xamb ´o 2015). The
key terms and characteristics of network music
include different types of network organization
(e.g., centralized versus decentralized or hierarchical
versus egalitarian organization), roles between
performers (e.g., soloist versus accompanist or sound
production versus sound modification), control (e.g.,
shared versus individual), and types of contribution
(e.g., parallel, serial, circular, or multidirectional)
(Xamb ´o 2015). This is helpful to understand the
nature of network music examples and how live
coding takes place using MIR on a musical network,
typically applied to live remixing multiple audio
streams.
Figure 10 outlines a block diagram of the audio
remixing approach, where the live coder receives
multiple audio streams (microphone, line in, output
of the system, etc.). The real-time analysis of the
audio signals is used to help the live coder taking
semiautonomous decisions about the live remix.
Xamb ´o et al.
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Figure 9. Example of code
of the audio-rewiring
prototype.
// Instantiation
a = MIRLCRew.new( 1, “afro-beat-6-8-toms.wav” )
// Starting onset detection with a beep style sound
a.onsets( ’beep’, 1 );
( r = Routine( { // Creation of a control structure for sequencing
var delta, option;
loop {
delta = 4.0;
delta.yield; // Amount of time in seconds until the routine should execute again
// If the modulus of 4 from the total number of onsets has remainder 0
// (4 clockwise sequence)
if( MIRLCRew.counter % 4 == 0,
{
// Choose with equal chance one of the four options
option = [ ’pitch’, ’beats’, ’onsets’, ’amps’ ].choose;
case
{option == ’pitch’}
{ a.pitch } // Pitch follower
{option == ’beats’}
{ a.beats } // Beat tracker
{option == ’onsets’}
{ a.onsets(’beep’, 1) } // Onset detector
{option == ’amps’}
{ a.amps(’spark’) } // Peak amplitude tracking with a percussive sound
});
}
} ); )
r.play // Play the routine defined above
Examples of Audio Remixing
There are illustrative examples of systems that
use MIR in real time for mediating multiple audio
streams, such as algorithmic systems, for instance
the pieces “Union” and “Flock” by the Orchestra for
Females and Laptops (OFFAL) (Knotts 2016), and live
sound visualization systems, such as FEATUR.UX
(Olowe et al. 2016). The use of multiple audio input
streams combined with MIR adds complexity to the
live coding environment. This approach twists the
role of the live coder towards taking organizational
decisions from incoming audio streams (e.g., setting
volumes of the audio streams or creating mappings
between audio and visual parameters), as opposed to
interacting with a single audio stream and creating
sounds, as in audio rewiring. The role of the live
coder thus needs to be redefined, where the use of an
algorithmic system to help take live decisions in the
performance space can lead to creative outcomes, as
shown in the pieces “Union” and “Flock.” In the
algorithmic examples, decisions about audio mixing
are based on features such as loudness. As shown
in FEATUR.UX, there is room for more-complex
mappings using both low-level (e.g., RMS, spectral
centroid, or MFCCs) and midlevel features (e.g., peak
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Figure 10. Block diagram
of audio remixing.
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frequency or chromagram) that are mapped to visual
attributes (such as size, shape, color, rotation, or
position), which are combined with a visualization
of the real-time changes.
Prototype 3: A Case Study for Exploring
Audio Remixing
This prototype is designed for supporting the
remix of multiple audio streams using MIR tech-
niques in SuperCollider (see the example online
at http://vimeo.com/249997569). This prototype
is conceptually inspired by the network music
piece Transmusicking I (see Figure 11, online at
http://youtu.be/AfR6UFS7Wuk), performed inter-
nationally by the Female Laptop Orchestra (FLO)
and Women in Music Tech (WiMT). The lessons
learned from using the Web audio interface WACast-
Mix (http://annaxambo.me/code/WACastMix/) in
this performance, as well as the experience from the
audio rewiring prototype, inform the design of this
prototype in terms of using feature extraction for
supporting spatialization, equalization, and mixing
of the incoming audio streams.
The role of live coding is less obvious in this
prototype, as it focuses on managing and mixing
the incoming audio streams. The challenge is to
use live coding procedures, such as representing
and manipulating the audio streams using code in
real time, as well as making the mixing process
visible to the audience. The use of multiple audio
streams requires careful attention from the live
coder, so visualization aids are acknowledged,
similar to the graphical user interface (GUI) used in
WACastMix. An interesting challenge is deciding
which audio streams are to be performed when,
Xamb ´o et al.
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Figure 11. Geolocation
diagram of a conjoint
performance between
Female Laptop Orchestra
(FLO) and Women in
Music Tech (WiMT).
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and how. The live coder uses functions based on
feature extraction to help make egalitarian decisions
(e.g., continuous versus sporadic audio streams or
vocal versus instrumental streams). This also avoids
unexpected problems, such as audio streams not
working momentarily. With respect to technical
challenges, this approach requires a high-broadband
Internet connection with low latency. There is also
the problem of network latency that (depending on
the type of connections of the musicians who are
sending audio streams) can be between 8 and 9 sec,
or even extend to more than a 30-sec delay. This is
a well-known issue in network music that affects
music synchronicity (Chafe, C ´aceres, and Gurevich
2010). Further explorations include combining
remote and co-located audio streams, developing a
GUI to support decision making, and mixing the
audio streams based on creative MIR ideas, such
as the multisong mashups from AutoMashUpper
(Davies et al. 2014).
Discussion
As shown in the literature review and illustrative
examples, MIR functionality in live coding envi-
ronments remains quite limited compared with the
state-of-the-art MIR systems outside these environ-
ments. There are several possible reasons for that.
First, a surprising number of MIR systems are not
designed for real-time use for the simple reason that
real-time capabilities are not typical design goals of
MIR researchers. Furthermore, many state-of-the-art
MIR systems require calculations that take more
time than the length of the processed audio block,
possibly leading to sound dropouts and high system
loads if used in a real-time context. Even if an MIR
system works in real time, its latency can be too
long for a given context. Second, real-time systems
are often not as reliable as offline systems. Offline
systems have access to more data for analysis and
can search for globally optimal solutions, or they can
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use iterative approaches with undefined processing
time. Third, the implementation of MIR systems is
often too complex nowadays, with too many depen-
dencies to be easily implemented as a plugin by a
nonexpert. Given that there is little overlap between
researchers in MIR and live coding, this impedes the
integration of advanced MIR technology. Fourth, the
focus of MIR researchers when designing systems
is frequently on a relatively narrow group of target
signals, such as Western popular musics. This makes
the systems both less usable and less appealing in
the field of live coding, with its tendency towards
experimental music. Of course there is information
that can, by definition, not be extracted in real time,
because systems require long-term context, such as
for the description of musical structure.
In our prototypes we have explored the extensive
use of midlevel features and some workarounds
to using high-level features (e.g., tag-based infor-
mation). We can conclude that the use of MIR
approaches in live coding is promising, yet it is
still in its infancy for reasons of complexity and
algorithm design, and also because of limited com-
munication between the fields. Opening a dialog
could further progress in both fields.
Conclusion and Future Work
In this article we have discussed MIR in live
performance, focusing on the improvisational
practice of live coding. In particular, we have
surveyed from the literature, and explored with
prototype making, three categories for using MIR
techniques in live coding: audio repurposing, audio
rewiring, and audio remixing. This article aims
to contribute to the live coding community with
new approaches to using MIR in real time, as
well as to appeal to the MIR community for the
need of more real-time systems, at the same time
offering an artistic outlet and usage scenario for MIR
technology. Next steps include the implementation
of machine-learning algorithms that can automate
some tasks of the live coder and could lead to
more-interesting musical results that evolve over
time, as well as the evaluation of these algorithms
from a musical and computational perspective. The
combination of the three categories is also of future
interest.
Acknowledgments
The authors wish to thank the reviewers and editors
for their insightful comments and suggestions,
which have helped to improve the article. We are
thankful to Gerard Roma, Anna Weisling, and
Takahiko Tsuchiya for inspirational discussions.
We appreciate the help and involvement of all the
collaborators who made this research possible. We
especially thank Frank Clark and Josh Smith from
the School of Music at the Georgia Institute of
Technology for their technical support. The work
presented in this article was partly conducted while
the first author was at the Georgia Institute of
Technology from 2015 to 2017 with the support
of the School of Music, the Center for Music
Technology, and Women in Music Tech. Another
part of this research was conducted while the
first author was at Queen Mary University of
London from 2017 to 2019 with the support of the
AudioCommons project, funded by the European
Commission through the Horizon 2020 program,
Research and Innovation grant no. 688,382.
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