A Multi-Touch DJ Interface with Remote Audience Feedback
Lasse Farnung Laursen1 , Masataka Goto2 , Takeo Igarashi1

2

1
The University of Tokyo, 7-3-1 Bunkyo-ku, Hongo, Tokyo, Japan
National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Japangazoo@42.dk, m.goto@aist.go.jp, takeo@acm.org
ABSTRACT

We propose a novel DJ multimedia interface that integrates state-of-the-art multi-touch table-top DJ controls with
communication functions, inspired by modern social media
platforms. The interface utilizes a time-line based visualization, supporting intuitive drag and drop element interaction
for easy audio cueing. Time-line elements are shown in detailor aggregated, as the user zooms in or out, respectively. This
allows for both an overview or in-depth look as needed. Our
system supports both a planning style visualization mode,
where audio elements remain static during playback, or amore dynamic visualization mode, where audio scrolls by
during playback. Both modes can be switched between in
real-time to suit the DJ’s current mood and needs.
The supported audience feedback includes a discrete positive ‘like’ which signifies a fondness for a particular occurrence in the performance, detailed comments from individual
audience members, and an audience size histogram. All incoming feedback is displayed along audio elements, in the DJinterface in real-time. We explore the new potential offeredby fusing feedback delivered by an unseen audience directly
into the multimedia interface of the DJ, shedding light on
the interaction both from the perspective of the DJ and theaudience.
A pilot study was conducted with volunteer DJs testingan early version of the interface, and participating in qualitative interviews. The positive response lead us to conduct
four live two-hour remote broadcasts with four DJs and a
volunteer remote audience providing real-time feedback via
our system. We gathered quantitative feedback from the audience and conducted qualitative interviews with the DJs.
Every show featured a minor, yet engaged audience, submitting feedback directly into our DJ multimedia interface.
All DJs showed a strong interest in receiving this feedback
during their remote performance, and some offered ideas as
to how it might improve their local performances, as well.

Current DJ interfaces lack direct support for typical digital communication common in social media. We present a
novel DJ interface for live internet broadcast performanceswith remote audience feedback integration. Our multi-touch
interface is designed for a table top display, featuring a timeline based visualization. Two studies are presented involving
seven DJs, culminating in four live broadcasts gathering andanalyzing data to better understand both the DJ and audience perspective. This study is one of the first to look closer
at DJs and remote audiences. We present useful insight for
future interaction design between DJs and remote audiences,and interface integrated audience feedback.

Categories and Subject Descriptors
H.5.2 [User Interfaces]: Graphical user interfaces (GUI);
H.5.5 [Sound and Music Computing]: Systems

Keywords
Touch Interface; Music Interface; Crowd-based Interaction;

1.

INTRODUCTION

Live broadcasting of video and audio streams via services
such as YouTube Live, Ustream, and Twitch.tv, has become
easy and popular. This type of self-broadcasting is an attractive and natural way for disc-jockeys (DJs)1 to remotely
reach a bigger audience. Traditionally, DJs inhabit a local
performance space together with their audience, often involving a significant amount of direct and indirect communication in-between them. This includes subtle visual cues,
such as head nodding, smiling, dancing and even occasionalverbal communication [4]. However, in the online performance space, new methods of interactions have establishedthemselves. DJs have access to chat rooms where they candirectly communicate with the audience, and in some caseseven receive a video stream from individual listeners [9].

2.

RELATED WORK

Nielsen ratings form the foundation of audience feedback
collection, covered by Beville [2]. However, Nielsen ratingsand most of the traditional systems described by Beville are
passive in nature. Feedback is evaluated posthumously andnot in the context of a live performance. A more recent
live audience feedback system is ‘The Cinematrix Interactive Entertainment System’ [3] presented at SIGGRAPH in
1991, and further expanded upon by Maynes-Aminzade etal. [7]. Although audience feedback is handled in a live context, there is no active performer involved other than theaudience itself. In this regard, our work more closely resembles that of Barkhuus and Jørgensen [1]. They present the‘Cheering-Meter’ used in free-style rap competitions withlive performers. However, contrary to our work, Barkhuus
and Jørgensen do not consider interaction with a remoteaudience.

1
In this paper we refer to radio/club DJs who play and blend
multiple musical compositions sequentially, rather than a
hip-hop DJ who uses turntables in a more instrumental way,
via percussive scratching sounds.

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Incoming ’Like’ from familiar audience member
Incoming Audience Likes

D

A

Incoming ’Like’ from unfamiliar audience member

Listener Count Histogram(Highs in Green, Lows in Purple

C

Open Song Dropdown List

Audio Elements

Incoming Audience Comments

Playback Line
Comment Pop-up

E
Broadcast Controls and
VU Meters

B
Play/Stop (Currently on Play)

Toggle Show Bronze Likes

Toggle Show Gold Likes

Playback Visualization Mode Toggle

Toggle Show Bronze Comments

Toggle Show Gold Comments

Toggle Listener HistogramFeature added in advanced prototype

Figure 1: The advanced prototype DJ Interface. Significant additions from the early prototype interface arehighlighted by the [2nd] marker.
Contemporary DJ instruments indirectly support remoteaudience feedback via existing online broadcasting portals.
However, this feedback is presented out of context, in a separate interface, dividing the DJ’s attention. Shamma et al.
[9] investigate remote DJ performances on such a portal, thenow defunct Yahoo! Live website. They focus on the community and social aspects of a shared online presence, rather
than concrete interactions during live performances.
Gates et al. [4] analyze how DJs gain audience awarenessduring local performances and present a set of principals for
how technology should support this activity. Their principals have influenced our design, despite the fact that theirfindings are based exclusively on local DJ performances.
Hook investigates interaction design for live performances[5], and provides a comprehensive look of many related live
performance interfaces. However, performer-audience interaction is left as future work.
Sgouros [8] presents an interaction framework, enabling
multiple users to take part in a performance either as aplayer or spectator and influence the outcome in real-time.
This work shares conceptual similarities with ours, but focuses more on the technical aspects of the system rather
than an evaluation of their users experience thereof.
Karnik et al. [6] present an ethnographic study of how
DJs use social media as part of their work. In contrast to
our work, their focus is exclusively on the use of social media
outside of a live performance context.
A vast number of audience response systems also exist in
the educational sector. It is important to note that although
our work shares similarities with these types of systems, thecontext for our work is fundamentally different, e.g. a higher

focus on cognitive workload and presentation is essential for
our interface.

3.

INTERFACE DESIGNOver the course of this work, our interface was tested at
two significant stages of its iterative development. Here we
describe general features of the interface. Differences between the early- and advanced prototype versions are detailed further below. The advanced prototype interface is
depicted in Fig. 1, with significant additions - from theearly prototype - highlighted.
The interface consists of a common audio time-line visualization (referred to as the mixmap) marked A , general
playback and visualization controls at the bottom marked
B , and a set of drop-down menus for loading audio marked
C . The entire mixmap supports pinch gesture zooming
for an ease of overview and/or detail. All loaded audio
elements support intuitive drag-and-drop style interaction.
Two modes of visualized playback are supported; static mixmap with a moving playback line, or moving mixmap with astatic playback line. The former is ideal for planning ahead
and setting up audio elements in advance. The latter modeof visualization is a more dynamic ‘on-the-fly’ style interaction mode. Switching between the two modes is possible atany time to suit the DJ’s current mood and needs.
Differences between the early- and advanced prototype
versions are detailed here. Changes were made to betteraccommodate live internet broadcasting, improve integrated
audience feedback, and reflect the DJ’s feedback on the earlyinterface. In the early prototype, integrated user feedback
consisted of four types of discrete feedback, which would

1226

A

Audience Counter
Shoutcast Hyperlink’Like’ Button

Chat Interface

B

Figure 2: The web-based audience interface.
appear in the area marked D . Audience members could
send the DJ a ‘like’, ‘dislike’, ‘volume up’, and ‘volume
down’ message. The advanced prototype interface simplified this feedback, retaining only the ‘like’ feedback, as theDJs mostly commented on its particular usefulness and werewary of overwhelming negative feedback. In accordance withthe feedback obtained during the pilot study, the advanced
prototype supports categorized listener feedback. Feedback
from unfamiliar and familiar listeners are now distinctly displayed in bronze and gold, respectively.
To more faithfully replicate existing online self-broadcasting
portals, we also chose to implement support for user comments (similar to Soundcloud) in the advanced interface,marked E . The mixmap was also enhanced to include an
integrated listener count histogram visualizing local minima
and maxima. This provides the DJ a clear overview of thehighs and lows of their performance, based on the size of theaudience.
The advanced interface also supports feedback filtering as
our pilot study indicated a strong preference for this. Audience histogram, bronze and gold ‘likes’ and comments caneach be turned on and off as needed in real-time. The dropdown menus were also improved to enable drag-and-drop
interaction, allowing users to easily drag unloaded audio on
to the mixmap. Zooming was improved to aggregate discrete‘like’ feedback into larger chunks, to ease the DJ’s overview,
as shown in Fig. 1 (where two gold likes are aggregated).
Finally, the advanced interface also adds support for microphones allowing the DJs to respond to any audience feedback
and further interact during a performance.
For the live broadcasts, a web-based interface was implemented to enable audience participation, shown in Fig.2.
Submitting a ‘Like’ or a comment produces a drop-down
message at the top of the page (marked A ) informing theuser whether or not it was delivered successfully. Comments
are submitted anonymously if the user refrains from entering a username in the interface, marked B . During a pilot
study, audience members expressed interest in viewing fellow listeners comments, which we added support for.

4.

EARLY PROTOTYPE PILOT STUDY

Previous work [4, 5, 6] indicates that DJs have an innate desire to please audience members through their performance, and as a result gather both indirect and direct
feedback by observing them. Although some forms of more
direct feedback (e.g. song requests) are generally frowned
upon. During a remote performance, however, the DJ andthe audience no longer inhabit that same performance space,and almost all feedback becomes direct as a result.
To further investigate this and related issues, we conducted an in-lab local pilot study of an early prototype ver-sion of our interface with five DJs, between the ages of 26-37;
four veterans each with 5 to 15 years of experience DJing
live in clubs, and one newcomer.
Qualitative feedback was gathered via think-aloud usage
sessions, where the DJs were encouraged to freely explore theinterface. Given that the performance was not broadcast,audience feedback was simulated by an on-site researcher.
Each DJ was interviewed to get insight into their perception of receiving feedback in general, and how local/remoteperformances differ in terms of wants/needs.
We define local feedback, as being traditional types of audience feedback present during a local performance (e.g. dancing, smiling, head nodding, verbal communication, etc.).
Remote feedback is defined as digital audience feedback commonly found in modern social media (e.g. text messages,
likes/dislikes, other discrete expressions of opinion).
All DJs professed a strong interest in receiving remote
feedback during a remote performance, and merely tentatively interested in receiving it during a local performance.
This is not surprising, given the lack of traditional meansof feedback gathering when performing remotely. One DJ
noted that he had a high interest in knowing where the feedback originated from and consequently how credible it was(random listeners vs. friends/other DJs), corroborated by
Karnik et al [6]. All DJs preferred more vague remote feedback during a local performance (i.e. like/dislike), as opposed to precise ratings (i.e. 3/5), partially due to the already high number of distractions in a nightclub setting.
As to whether the DJs were likely to change a local performance based on remote feedback, some DJs stated they
were likely to disregard it completely. Others valued any
and all information they were given. Surprisingly, a few DJs
stated they were even less likely to react to remote feedback
during a remote performance.
Phillip Huey: “I wouldn’t do a live [remote] mix, and thenget some negative feedback, and [...] just switch genres. I
might do that at a [local] club.”
The DJs’ opinions also varied in regards to how usefulremote feedback was, compared to traditional local feedback.
Matthew Gammon: “A like/dislike is very binary and too
simple. Seeing someone dancing [...], there are so many
non-verbal and verbal cues we can read off of individuals,[...]. Having a lot more to draw from, makes the interpretation easier to go through. [...] With [(likes/dislikes)] you
don’t know how much they like it or how much they don’t
like it.”
DJ Modality: “The feedback was helpful. Particularly if I
was doing an online set, that would be really useful. [Thefeedback] is more direct. If we use a dance floor analogy;
People will leave the dance floor [due to fatigue or to get adrink,] not cause they don’t like the music [...]. [...] So inthat way, dancing is kind of an imprecise metric.”
Despite the fact that previous research paints a very dimview from DJs regarding direct song requests from the audience, some of our volunteers still expressed an interest inreceiving them via remote feedback.

5.

LIVE BROADCAST STUDY

We conducted a live broadcast study with four male DJs,
two returning participants, between the ages of 28-33; eachwith 2 to 11 years of experience DJing live in clubs. Each DJ

1227

Day
Likes Com.
Min Max Avrg
ALSof Show(B/G) (B/G)
Sunday
96
111
104
38/4
96/28
14
Tuesday
121
152
134
13/4
84/8
7
Thursday 131
157
145
29/1
85/21
12
Sunday94
122
104
14/15 58/52
13

participated in a live 2 hour internet broadcast show (airing
20:00 local time) broadcast from our lab, and acting as theproverbial host, being in control of the interface for the duration of the performance. An on-site researcher was present
to aid the DJ in any issues that might arise unexpectedly, as
well as setup and shutdown the broadcast. No other people
were in direct attendance and the stations ‘regular’ audience
tuned into the broadcast via the internet. Audience interaction was encouraged vocally as part of the broadcast, andaccess to the web-based interface was provided via generallink on the internet radio homepage. Friends of the DJ wereindividually directed to a unique link, distinguishing themfrom other listeners. Interviews were conducted with eachDJ both before and after the live broadcast to collect qualitative data. Audience members were asked to fill out an
online survey after the conclusion of the broadcast.
We observed the DJs having a preference for the more
dynamic visualization mode, as only one of them used thealternative static mode for the majority of their broadcast.
Our interfaces were well received by both the audience andthe DJs. Both parties frequently engaged one another forthe duration of the broadcasts.
Every DJ specifically commented on the usefulness of receiving direct feedback (i.e. likes/comments), as well as indirect feedback (Nr. of listeners) during the shows.

Table 1: Quantitative audience data showing the dayof each show, Min/Max/Average number of listeners, Nr. of likes and comments (bronze/gold respectively), and Nr. of answered listener surveys (ALS).
passive views/listens far outranks the NR. of likes/comments.
Contrary to these sites, however, our audiences delivered far
more comments than likes. Just like the DJs, the audiencesurvey answers indicate comments being preferred to ‘likes’.
Apart from a single listener, all who participated in the surveys expressed an interest in providing the DJ with feedback.
Their interest in providing a local live DJ with similar feedback was present, but less strong.

6.

CONCLUSION

We performed two qualitative studies, presenting a novel
multimedia DJ interface, and a closer look at how DJs perceived local and remote feedback. Our work builds upon previous research and shows a more nuanced landscape in termsof the DJs wishes/needs for this evolving technology. Every
DJ showed a clear interest in receiving integrated feedback
during remote performances. Some DJs also expressed aninterest in receiving this feedback in a local context. We believe local feedback integration is most useful in situations
where direct interaction with individuals is limited, such aslarger concerts or festivals. Similar to Barkhuus and Jørgensen [1], we posit that the area of audience-performanceinteraction is ripe for further exploration.

Mark Jackson: “It gives you inspiration and energy. [...]
It works. I was surprised.”
The DJs generally preferred comments to ‘likes’, noting it
showed more of an effort on behalf of the audience members,
and leading to more informed performance improvements.
None of the feedback was toggled off during the live broadcasts. Our observations and quantitative data, shown in Table 1, indicate that the feedback never became overwhelming, and thus required filtering. Its persistent use during
broadcasts is an indicator that the DJs found it to be neither intrusive, nor annoying. Some DJs also professed aninterest in receiving remote feedback during a local performance, although one provided a more hesitant response.

7.

ACKNOWLEDGMENTS

We thank all seven DJs who participated in our user study,
Slayradio.org, the associated staff for their assistance, andall our listeners. This work was supported by JSPS KAKENHI Grant Number 24-02734.

Matthew Gammon: “It’s interesting to see how people react
and what they do. Will it heavily influence what I do? I
don’t think so.”8.

REFERENCES[1] L. Barkhuus and T. Jørgensen. Engaging the crowd. In Proc.
ACM CHI ’08, pages 2925–2930, 2008.
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Routledge, 1988.
[3] L. Carpenter and R. Carpenter. Cinematrix interactive
entertainment system, 2001. http://www.cinematrix.com.
[4] C. Gates, S. Subramanian, and C. Gutwin. DJ’s Perspectives
on Interaction and Awareness in Nightclubs. In Proc. ACM
DIS ’06, pages 70–79, 2006.
[5] J. D. Hook. Interaction Design for Live Performance. PhD
thesis, Newcastle Univ. School of Comp. Sci., 2013.
[6] M. Karnik, I. Oakley, and V. Nisi. Performing online andoffline: How DJs use social networks. In Human-Computer
Interaction–INTERACT 2013, pages 63–80. Springer, 2013.
[7] D. Maynes-Aminzade, R. Pausch, and S. Seitz. Techniques
for interactive audience participation. In Proc. IEEE ICMI
’02, page 15, 2002.
[8] N. M. Sgouros. Detection, analysis and rendering of audience
reactions in distributed multimedia performance. In Proc.
ACM MULTIMEDIA ’00, pages 195–200, 2000.
[9] D. A. Shamma, E. F. Churchill, N. Bobb, and M. Fukuda.
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DJs. In Proc. ACM C&T ’09, pages 175–184, 2009.

One of the DJs saw a strong potential in data mining thecollected remote feedback to learn more about the audience,even after a local performance. Karnik et al. [6] corroborate
this point.
Dj Vivid: “It would help me understand my audience better,know what they want to hear, which could lead to better
parties or sets. That’s important. [...] I can see a lot ofuses for that.”
Another DJ hypothesized that some audience members who
would normally not provide direct local feedback, would be
more inclined to provide remote feedback.
Our expectation that DJs value feedback differently depending on the origin (random listeners vs. friends/otherDJs), was not strongly confirmed. Only one DJ professed
an interest in distinguishing the origin.
During the shows we observed an engaged minor portionof the audience, as indicated by the gathered quantitative
data in Table 1. This is consistent with social media platforms such as YouTube and Soundcloud, where the more1228