The ILO would like to thanks Farhad Mehran for his assistance with the Classification Tree methodology.

Funding for this ILO publication was provided by the United States Department of Labor (Project GLO/09/56/USA). This publication does not necessarily reflect the views or policies of the United States Department of Labor, nor does mention of trade names, commercial products, or organizations imply endorsement by the United States Government.

The designations employed in ILO publications, which are in conformity with United Nations practice, and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the International Labour Office concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its frontiers. The responsibility for opinions expressed in signed articles, studies and other contributions rests solely with their authors, and publication does not constitute an endorsement by the International Labour Office of the opinions expressed in them. Reference to names of firms and commercial products and processes does not imply their endorsement by the International Labour Office, and any failure to mention a particular firm, commercial product or process is not a sign of disapproval. ILO publications and digital products can be obtained through major booksellers and digital distribution platforms, or ordered directly from ilo@turpin-distribution.com. For more information, visit our website: www.ilo.org/publns or contact ilopubs@ilo.org.

Table of contents

Introduction

Many children are still engaged in hazardous work despite national laws and international Conventions. Such work is likely to damage their physical health, but does it also have an effect on their psychological health, development or social integration? If so, how do working children perceive their current lives and future? Does work help them develop certain coping skills, or does it undermine them? Is the psychosocial profile of working children different in any way from that of their non-working peers? Are working girls and boys affected differently by work?

Previous studies relating to working children's mental health have been largely clinical observations [START_REF] Boidin | Labour inspection and the adoption of a policy in child work. The working child: Psychosociological approach[END_REF] or country-based studies [START_REF] Fekadu | The prevalence of mental health problems in Ethiopian child labourers[END_REF]ILO-IPEC, 2014c;[START_REF] Loughry | Assessing Afghan children's psychosocial well-being: A multi-modal study of intervention outcomes[END_REF][START_REF] Alem | Child labor and Childhood behavioural and mental health problems in Ethiopia[END_REF], a noteworthy exception being a recent multi-country study of child domestic workers [START_REF] Gamlin | Is domestic work a worst form of child labour? The findings of a six-country study of the psychosocial effects of child domestic work[END_REF]. While these are most valuable, what has been lacking is cross-cultural statistical data which would enable us to have a firmer grasp on the scope and nature of the effects of work on children's psychosocial health.

This paper analyses the results of the psychosocial component of a larger research project on the health of working children which was undertaken by the ILO. This four-country study, carried out in Afghanistan, Bangladesh, Nepal and Pakistan, sought to examine the degree to which health, and especially mental health, might be a critical variable to be considered in policy and action to address child labour (ILO-IPEC, 2011a[START_REF] Gamlin | Is domestic work a worst form of child labour? The findings of a six-country study of the psychosocial effects of child domestic work[END_REF][START_REF]A health approach to child labour: Synthesis report of four country studies on child labour in the brick industry[END_REF], 2014b, 2014c[START_REF]A health approach to child labour: Synthesis report of four country studies on child labour in the brick industry[END_REF]ILO-UNICEF, 2015). The study used a crosssectional design and collected both quantitative and qualitative data on physical and mental health and the work/home environment.

The study population consists of

Methodology

Investigating child workers' health presents a number of challenges because the methods must be applicable to low-resource situations where child labour is prevalent but where medical, psychological or research professionals are not present. An assessment tool was specially developed to measure children's psychosocial health in an occupational setting, called the "Instrument for Psychosocial Assessment of Working Children" (IPAW). This instrument was based on the theoretical work of [START_REF] Woodhead | Psychosocial impacts of child work: a framework for research, monitoring and intervention[END_REF] with contributions from others (Camfield et al., 2009 1 ; ILO-IPEC, 2013c; Jordans et al, 2011; ILO-IPEC, 2011) and underwent an extensive period of testing and validation prior to its use in the study.

The working children were administered the full 48-item IPAW questionnaire and the comparison group were given a 31 item version (work-related questions omitted)2 ; both used a Likert scale of 4 degrees (never, sometimes, often, always). The objective was to identify areas of psychosocial functioning in which the children who are working in the brick kilns differ from their peers who are not working in the kilns.

Classification Tree Analysis (CTA) was chosen as the strategy for analysing the data, using the R statistical software. This exploratory technique is particularly useful for complex data where there are missing responses or confounding variables which is often the case in research being undertaken in challenging conditions and environments. With this model, it is possible to distinguish the psychosocial characteristics of working children from those of non-working children on the basis of their responses to a set of questions.

The CTA generates a multi-level diagram in the shape of a tree, with "branches" being the relationships and the "leaves" being mutually exclusive subgroups of individuals.

This presentation of the data on the child brick workers statistically demonstrates which items among many are most important in showing differences between working and control children. The CTA identified a set of eight questions in the IPAW which are particularly explanatory [START_REF] Morgan | Classification and regression tree analysis[END_REF] By knowing the responses of a child on these eight items, we can predict whether or not s/he is a brick kiln worker with a minimum risk of error. Put another way, on the basis of the children's responses on these eight questions nested from top to bottom, each level offering a further refinement of the previous one, the Classification Tree Analysis identifies the psychosocial characteristics on which children who are working in brick factories differ from those who are not (Figure 1).

Figure 1. Percentage of children working and non-working in brick factories

The top or the "root" of the tree is the total sample of 1.555 children, composed of 41 per cent non-working and 59 per cent working children. At the bottom of the figure are the "leaves", i.e. the characteristics which differentiate working children from nonworking children. In between, are 7 "nodes," or points of comparison.

Example: Analysis of the first node

At the root (which explains 31 per cent of the variance between workers and controls), there are two groups of children on the item "Do you feel accepted by the other families around here?" The split between the two groups is roughly on a score of 2.5, which is in the exact middle of the Likert scale graduated from 1 (never) to 4 (always). Of these, 52 per cent of the sample have scores superior or equal to 2.5 on this question, which means that the majority of the children feel "often" or "always" accepted by families around them. In this group of children, those who work are significantly less numerous than those who do not work (37 per cent vs. 63 per cent). Since 48 per cent have lower scores (scores not superior or equal to 2.5) and report feeling "never" or only "sometimes" accepted by families around them, they experience a sense of rejection.

Here, the workers represent 82 per cent of the group, the non-workers represent only 18 per cent. The proportion of workers is low in the more positive group in terms of wellbeing (feeling accepted) and it is very high in the more negative one (feeling rejected).

Looking at it from a predictive point of view, the classification tree indicates at this stage that if a child does not feel accepted by other families around him or her, it is likely that s/he is working. At this stage, the error rate of such a classification is high, about -10.6 percent, which means that 134 non-working children could be wrongly classified as 'working' and 299 working children could be wrongly classified as 'not working'.

Such an analysis is carried out at each of the succeeding nodes of the classification tree. At each of these points, the psychosocial characteristics of the groups are increasingly refined and the likelihood of error is reduced.3

Discussion

The Classification Tree Analysis (CTA) developed for this study is fairly parsimonious with four levels of segmentation resulting in nine "leaves" or discrete groups and it appears to be highly effective in predicting if a child is working or not in a brick kiln. The analysis which is carried out at each node of the classification tree shows, at each stage, a closer relationship between work status and the psychosocial status of the child. At the end of the process, the results indicate that by knowing these eight elements of a child's psychosocial wellbeing, we can predict whether the child is working or not working in the kilns with an acceptable error rate of about 1.5 percent. The nine leaves correspond to nine psychosocial profiles of children (Figure 2).

Figure 2. Psychosocial profiles of children

The largest category (43 per cent of the entire sample) is composed of children who do not feel accepted by the surrounding families and do not feel confident. These feelings are not consistent with a strong sense of well-being. In this group, 86 per cent are children who work in the brick factories.

The second largest category, representing 32 per cent of the entire sample, is composed of children who feel socially accepted, safe and optimistic. Among them, only 18 per cent are working children.

The six other categories of children are much smaller (they represent 1 to 6 per cent of the entire sample). There, the working children represent the major part of the children who do not feel accepted by others (i.e. feel rejected), do not feel safe (i.e. feel threatened), do not feel confident, are tired and have little hope for the future. Some among them do not play games or sports and feel very isolated (not having friends to support them).

Overall, kiln workers are under-represented among the 1 st , 3 rd , 6 th and 7 th leaves (1 st and 3 rd are the more positive profiles in terms of wellbeing) and they are over-represented on the 2 nd , 4 th , 5 th , 8 th and 9 th leaves (4 th , 5 th and 9 th are the more negative profiles in terms of wellbeing) (Figure 3).

Figure 3. Proportion of non-worker and worker children

With this classification tree analysis, we can predict with a minimal risk of error that if children do not feel accepted by other families around them and do not feel self-confident (Leaf 9) then they are working in the brick kilns. It is also likely they are working if they are feeling confident and no different from others, but at the same time agitated or uneasy (Leaf 8). If they feel accepted but not safe and never play sports or games with friends (Leaf 5) or when they have leisure and play but have no friends to support them (Leaf 4), it is also likely that they are working. If they feel accepted and safe in their daily life, when they think that their life will never get better (Leaf 2), it is also likely that they are working. On the contrary, if a child feels accepted by other families, feels safe in her or his day to day life, and thinks that life will get better someday (Leaf 1), we can predict that s/he is not working in the kilns.

The relationships between the items identified by the CTA do not change with the gender of the child: girls share the same overall psychosocial characteristics as the boys.

These results indicate that children working in the brick kilns have a significantly higher risk of psychosocial distress than their non-working peers. Eight fields of psychosocial wellbeing are what account for the difference. These are: the degree to which they feel accepted by others, have a sense of confidence and safety in daily life, look forward to a better life, have friendships, engage in leisure activities (games and sports), feel calm or restless, and have a sense of belonging. It should be noted that the results obtained from the classification tree analysis do not indicate the direction of causality or whether there might be other influential factors, e.g.: is it work in brick kilns that negatively affects the psychosocial wellbeing of the children or do the children come to their work in the kilns with already a different set of psychological characteristics? Further research, especially longitudinal studies, could help to clarify this.

This analysis is important for future surveys on psychological risks to adolescent workers in that it reduces the possible variables to those which are crucial to include, i.e. the eight psychosocial questions noted above. In fact, it is the first three or four items (questions 39, 48, 19 and 34) which are especially crucial in that they explain respectively 49 to 51 per cent of the variance between workers and non-workers. Inclusion of these items in a survey or study is sufficient to give an indication of psychosocial impairment.

Conclusions

The Classification Tree Analysis (CTA) presented here, using the example of children working in brick kilns, demonstrates that there is a strong relationship between work and children's psychos are significantly disadvantaged compared to non-working children from the same environment. The results of the analysis present the first statistically-based justification for giving high priority to the children working in brick kilns on the basis of the psychosocial risks alone. The psychosocial data is particularly important as it shows that these children need assistance in many different ways, not just simply being removed from work. Providing them with education is probably the most effective response, but this needs to be accompanied by other forms of psychosocial support such as opportunities for socializing and play. threshold on scores superior to 1,5. A large group of children (32 per cent) express a certain type of wellbeing, i.e. feeling 'accepted, safe, and optimistic.' The non-workers represent a large majority (82 per cent) while workers are only 18 per cent. The group of children who are feeling 'accepted, safe, but pessimistic' is composed of socially integrated children, who feel safe but who see no possibility of improving their situation and hence are pessimistic. Here, most (78 per cent) are workers.

There is a fourth node for children feeling 'accepted yet under threat', on the item "Do you play games or sports with friends?" There is a threshold on scores superior to 1,5 which means that when children answer no, they have no opportunity for play or leisure. One group of children plays sports and games with friends, one group never does. In it, 96 per cent are working children.

There is a fifth node for children who feel 'rejected yet confident' on the item "Do you feel very different from other children your age?" These are mostly non-workers (94 per cent). Of those who feel 'rejected, yet confident and not different from others', most are working children (55 per cent). One hypothesis might be that they feel rejected because, unlike the non-working children, they are engaged in work of low status, but yet feel confident because of the sense of belonging, camaraderie and similarity they have among their peers at work.

There is a sixth node on children feeling 'accepted and who play games and sports with friends, but feel under threat' on item "Do you have one or more friends that support you?" This group includes only 32 per cent of workers while those not having friends' support accounts for 72 per cent.

The seventh node is for the children who feel 'rejected but confident and not different from others' and refers to the item "Do you feel restless and cannot stay still very long?" On this node there is a threshold on scores smaller than 1.5 which corresponds to the answer "never restless". Of those who are 'rejected but confident, not different, and never tired', a majority (70 per cent of 1 per cent) are non-working. Those who feel fatigue (3 per cent of the entire sample) are largely working children (69 per cent).

Fundamental Principles and

1 .FiguresFigure 1 .

 11 FiguresFigure 1.Percentage of children working and non-working in brick factories ..............................................................................

Figure 2 .

 2 Figure 2. Psychosocial profiles of children ........................................................

Figure 3 .

 3 Figure 3. Proportion of non-worker and worker children ......................................

  . These relate to social acceptance, sense of security, optimism, social support, play and leisure, alienation, and anxiety. The precise items are:

	o	Do you play games or sports with friends?
	o	Do you feel very different from other children your age?
	o	Do you feel restless and cannot stay still very long?
	o	Do you feel accepted by the other families around here?
	o	In your day-to-day life do you feel safe?
	o	Do you think your life will get better some day?
	o	Do you generally feel pretty confident?
	o	Do you have one or more good friends that support you?

See also University of Oxford, Young lives longitudinal study into children and youth around the world, available at https://www.younglives.org.uk/.

Questionnaire used provided in Annex I.

The analysis at each of the succeeding nodes is described in Annex II.

Acknowledgements

This study was elaborated by Catherine Pellenq, Susan Gunn and Laurent Lima under the framework of the ILO project "Follow-up to the resolution on child labour statistics adopted at the 18th ICLS through methodological development and expansion of child labour data collection" (GLO/09/56/USA) implemented from September 2009 to December 2013.

Annexes

Annex II: Analysis of the succeeding nodes

At the first node, among the first group of children (the 52 per cent who feel accepted by the other families around them) there is a segmentation on the item "In your day-to-day life do you feel safe?" On this node, 36 per cent have scores superior or equal to 2.5 on this question. That means that most of the children feel safe. But 16 per cent have lower scores, indicating that they live under a sense of threat; they say that they "never" or only "sometimes" feel safe. In the group composed of the children who feel 'accepted and safe', those who work are significantly less numerous than those who do not work (24 per cent vs. 76 per cent). Conversely, in the second group composed of children who feel 'accepted but not safe', the workers are over-represented with 69 per cent, the nonworkers represent only 31 per cent. Once again, the proportion of workers is low in the more positive group (feeling accepted and safe) and it is high in the more negative one (feeling accepted but under threat). So if a child feels unsafe, one can predict that s/he is working.

At the second node, within the second group of children (the 48 per cent of those who feel less accepted by the families around them), there is bifurcation on the item "Do you generally feel pretty confident?" The difference between the groups shows a threshold on scores greater than 3.5 which is the highest score on the four levels of the Likert scale, corresponding to the answer "always". Very few children (5 per cent) feel confident when they sense they are not accepted by other families. Of the 5 per cent of children who "always" feel confident, those who work are less numerous than those who do not work (45 per cent vs 55 per cent). It is likely that this group of children is composed of minorities (migrants, ethnic or religious minorities), who feel rejected because of their status but at the same time confident because they are part of a social group. On the other hand, those who fall into the category of 'Not accepted (rejected) and not confident' which is composed of the 43 per cent of children who do not feel always confident, the workers represent 86 per cent of this group, the non-workers representing only 14 per cent. This final "leaf" represents the biggest group of the population and the proportion of workers in it is more than 6 times those of the non-workers.

Once again, the workers are under-represented in the more positive group (feeling confident) and over-represented in the more negative one (feeling unconfident). Looking at it from a predictive point of view, if a child feels unconfident as well as unaccepted by surrounding families, it is likely that s/he is working.

The second level of inquiry on this node "feeling safe in day-to-day life" among children feeling accepted by surrounding families and "feeling pretty confident" among children who do not feel accepted helps narrow the error rate of the classification and tightens the prediction of the work status of the child. At this stage, 49 per cent of the variance between workers and controls is explained.

At the third node, which explains 2 per cent more of the variance, the children divide into two groups on the item "Do you think your life will get better some day? There is a


Hugo Scurto
email: h.scurto@gold.ac.uk

Rebecca Fiebrink
email: r.fiebrink@gold.ac.uk

GRAB-AND-PLAY MAPPING: CREATIVE MACHINE LEARNING APPROACHES FOR MUSICAL INCLUSION AND EXPLORATION

We present the first implementation of a new tool for prototyping digital musical instruments, which allows a user to literally grab a controller and turn it into a new, playable musical instrument almost instantaneously. The tool briefly observes a user interacting with a controller or sensors (without making any sound), and then it automatically generates a mapping from this observed input space to the control of an arbitrary sound synthesis program. The sound is then immediately manipulable using the controller, and this newly-created instrument thus invites the user to begin an embodied exploration of the newly-created relationships between human movement and sound. We hypothesize that this approach offers a useful alternative to both the creation of mappings by programming and to existing supervised learning approaches that create mappings from labeled training data. We have explored the potential value and trade-offs of this approach in two preliminary studies. In a workshop with disadvantaged young people who are unlikely to learn instrumental music, we observed advantages to the rapid adaptation afforded by this tool. In three interviews with computer musicians, we learned about how this "grab-and-play" interaction paradigm might fit into professional compositional practices.

INTRODUCTION

Historically, computer programming has been a core technique used in the creation of new digital musical instruments. The "mapping" [START_REF] Hunt | Mapping performer parameters to synthesis engines[END_REF] that specifies how a musician's movements (sensed using a controller or sensors) relate to sound (e.g., the values of sound synthesis parameters) is often created by writing programming code. While programming allows a mapping to be specified precisely, the process of translating an intended mapping function to code can be frustrating and time consuming [START_REF] Fiebrink | Toward understanding human-computer interaction in composing the instrument[END_REF], even for expert programmers, and it is inaccessible to non-programmers.

Machine learning has been used as an alternative mechanism for generating mappings since the early work of [START_REF] Lee | Real time neural network processing of gestural and acoustic signals[END_REF]. Most work that has employed machine learning for mapping creation has employed supervised learning algorithms, which can create a mapping from input sensor values to sound synthesis control parameters using a set of Copyright: c 2016 Hugo Scurto et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 Unported, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

"labeled" training examples. In this labeled training set, each example consists of one vector of input sensor values, plus the "labels"-the vector of sound synthesis parameter values the designer would like to be produced in response to those sensor values. Research has suggested that supervised learning offers a useful alternative to programming, for instance by making mapping creation faster, by enabling designers to encode an embodied understanding of the desired gesture/sound relationships in the training examples, and by making mapping accessible to nonprogrammers [START_REF] Fiebrink | Toward understanding human-computer interaction in composing the instrument[END_REF][START_REF] Fiebrink | Real-time human interaction with supervised learning algorithms for music composition and performance[END_REF].

However, existing supervised learning approaches to mapping creation do not directly address some of the most fundamental needs of instrument designers. For instance, an instrument designer often does not know a priori precisely what type of mapping she wants in a new instrument. It is only by prototyping-experimenting with alternative designs in a hands-on way-that she can more fully understand the potential offered by a set of sensors and synthesis tools, and understand how she might fit these together into an instrument or a performance. An instrument designer who wants to explore many different prototypes using machine learning must still create many different sets of training data, and explicitly choose the type of relationship between sensors and sounds that should be embedded within each set. New approaches to mapping generation might accelerate the discovery and realisation of new design ideas, by taking advantage of the computer's ability to generate mapping functions under different types of constraints or with different types of goals. This could be useful when the user does not have a specific relationship between movement and sound already in mind, or when other properties of the instrument (e.g., playability, comfort) supersede any preference for a particular sensor/sound relationship.

In this paper, we describe first steps toward exploration of such alternative mapping strategies. We have implemented a fully-functioning tool capable of generating many alternative mappings from a single set of unlabeled training examples, which encode the range of motion of a performer using arbitrary sensors/controllers. In our first version of the system, alternative mappings are generated from this single training set using a computationally straightforward approach to transform the unlabeled training set into multiple alternative labeled training sets, which can each be used to build a mapping using supervised learning. Many other computational approaches to generating multiple alternative mappings from unsupervised learning are also possible.

We have worked with two sets of users to evaluate this approach and better understand its potential use. These users include youth with disabilities and difficult life circumstances, as well as three professional computer music composers. This work suggests that the rapid adaptation afforded by this approach could benefit the first category of users, while the predisposition to musical exploration and discoveries could benefit the second category.

BACKGROUND AND PREVIOUS WORK

Machine learning algorithms have been widely employed in musical interaction, both as a means to analyze musical gestures and to design gesturally-controlled digital musical instruments (see [START_REF] Caramiaux | Machine learning of musical gestures[END_REF] for an overview of the field).

Research by Fiebrink and collaborators has focused on understanding the impact of using machine learning (as opposed to programming) on the instrument design process [START_REF] Fiebrink | Toward understanding human-computer interaction in composing the instrument[END_REF], and on designing user interfaces to allow instrument builders to use machine learning effectively and efficiently, without requiring prior machine learning expertise [START_REF] Fiebrink | Real-time human interaction with supervised learning algorithms for music composition and performance[END_REF]. Fiebrink's Wekinator 1 toolkit allows instrument builders to create supervised learning training sets by demonstrating performer gestures alongside the instrument sounds the designer would like to be produced by those gestures. The Wekinator uses general-purpose algorithms for regression (e.g., multilayer perceptron neural networks, linear and polynomial regression) and classification (e.g., nearestneighbor, support vector machines) to create mappings from this data.

Other recent research has explored the development of new modeling approaches that are tailored to building gestural musical interactions [START_REF] Bevilacqua | Continuous realtime gesture following and recognition[END_REF][START_REF] Francoise | Motion-sound mapping by demonstration[END_REF], notably allowing for similarity estimations between a gesture being performed and recorded references. Such approaches are particularly successful when the task is to recognize and track given gestures.

There is a growing interest among music researchers in the importance of bodily experience in sound perception and cognition [START_REF] Leman | Embodied music cognition and mediation technology[END_REF]. According to this theory, it is primarily through the body that performers convey inner information about their artistic intentions and emotions; this bodily information is encoded into and transmitted by sound to listeners who can in turn attune to the performer's inner intent. It is important to underscore that such body movements, or gestures, are not necessarily pre-defined for the performer, and can appear to be metaphorical [START_REF] Godøy | Musical gestures: Sound, movement, and meaning[END_REF] rather than descriptive [START_REF] Caramiaux | The role of sound source perception in gestural sound description[END_REF][START_REF] Scurto | Combining gestures and vocalizations to imitate sounds[END_REF]. In this sense, mapping approaches that value exploration rather than explicit definition could be relevant to facilitate the use of metaphorical gestures in performance.

GRAB-AND-PLAY MAPPING

Definition

We propose a new paradigm for mapping creation, called "grab-and-play mapping", that enables the very rapid creation of new instruments from a very small amount of data communicating some minimal, soft design constraintsnamely, the way the user might want to move while playing 1 www.wekinator.org this new instrument. This minimal set of data allows the creation of mappings which are customised to a controller and/or to a player in a loose sense, by aiming for a mapping that is playable using whatever range of motion and types of variation are present in the examples provided by the designer. But this process does not require a designer to specify other information about the instrument, other than potentially the range of legal values for each sound synthesis parameter that will be controlled by the mapping. Our approach thus shifts the designer's focus from one of imagining and then implementing new gesture-sound relationships, to a focus on discovering new relationships that have been designed partly by the computer, and on embodied exploration of those relationships.

Implementation

Our vision of grab-and-play mapping could be implemented using a number of techniques for automatically generating a mapping. This paper reports on our first implementation, which is described in Figure 1.

In this implementation, the user must first demonstrate how she will physically interact with a musical controller; this results in a recorded, continuous stream of gestural input data. Next, the computer transforms this stream of unlabeled inputs into a labeled training set that can be passed to a supervised learning algorithm for mapping creation (e.g., a neural network). Specifically, a number of examples are chosen at random from the recorded inputs. Each example is assigned a randomly-generated value for each sound synthesis parameter. These random sound synthesis parameters could be chosen from user-selected "presets" (i.e., vectors of parameter values that, together, result in sounds the user might want to have present in the instruments). Or, each parameter could be randomly generated from a uniform distribution over the range of all legal parameter values (e.g., [0,1]).

Finally, this artificially-generated training set is fed into a supervised learning algorithm that builds a mapping function capable of computing a new sound synthesis parameter vector for any new control vector. The user can now play the newly-created instrument by interacting with the Observational studies reported in this paper only used the random implementation for input; random implementation and preset implementation for output. Other implementations of the grab-and-play approach are already implemented (see section 6), and will be studied in the near future.

input controller or sensors and discovering how the sound changes with her actions.

This new tool is implemented in Java as a branch of the Wekinator software. All code is available online 2 . The new tool adds the following additional functionality to Wekinator (see Figure 2):

• Grab-and-play mappings can be generated using the procedure above, requiring only that the user demonstrate a brief input sequence. • New alternative mappings can be generated indefinitely from the same grab-and-play unlabeled training sequence. • The user can interactively change the number of su-

pervised training examples randomly generated from the grab-and-play training sequence. • The user can switch between grab-and-play mapping and mappings generated using supervised learning. The tool also takes advantage of the following existing capabilities of Wekinator:

• Any type of input controller or sensor system can be used to control sound, provided data about the input is sent as an OSC message [START_REF] Wright | Open sound control: A new protocol for communicating with sound synthesizers[END_REF]. • Any sound synthesis software can be used to play sound, provided it can receive synthesis parameter vectors as OSC messages. • The GUI allows users to switch immediately and repeatedly between generating mappings and playing the generated instruments in real-time. ping problems, so novice users never have to make an explicit choice of learning algorithm or algorithm parameters.

PRELIMINARY WORKSHOP WITH DISABLED YOUNG PEOPLE

We used this tool in a workshop with disabled young people to gain a preliminary understanding of how it might be useful for building new musical instruments for people with disabilities, and of how youth might respond to the customised yet unpredictable mappings built by this tool.

Using machine learning to build instruments with disabled people

Machine learning has been recently applied to build custom musical interfaces for disabled adults through several workshops [START_REF] Katan | Using interactive machine learning to support interface development through workshops with disabled people[END_REF]. Not only did the authors of that work find similarities between the musical goals and practices of disabled people and expert musicians, but they also noted some difficulties for participants to develop a memorable gestural vocabulary. In our workshops, we were therefore curious whether this grab-and-play approach might circumvent some user frustration, by explicitly inviting exploration of new instruments rather than suggesting that gesture design and memorisation are important.

Workshop setup

Participants

The workshop we led was the first workshop of a Musical Inclusion Programme3 , one of the aims of which is to help disadvantaged young people take part in musical activities. "Disadvantaged" stands for a broad variety of living conditions, ranging from health, behavior or learning disorders to looked-after children. Such young people may not have the opportunity to access high-quality musical activities, thus preventing them from the benefits music can provide in a social context. Bespoke digital musical instruments have the potential to make music-making easier and more fun for many of these youth. It is also possible that using personalised instruments may reduce social pressure, since the mapping function is unique to each user. By emphasising participation as a process of exploration of instruments and sound rather than performing a piece of music correctly, we also hoped to make the experience fun and inclusive for everyone.

The 15 youth we worked with all had physical and/or mental disabilities. They were accompanied by their parents or guardians, and their level of concentration was variable depending on their disabilities.

Workshop structure

The workshop was a one-hour session during which each of the two workshop leaders led a sequence of small-group sessions with one youth participant and their parent/guardian(s).

The input device used was a GameTrak Real World Golf controller, which senses 3D position of the user's hands using two strings. Sound was generated by Max/MSP. The following setups were available to the participants:

• Grab-and-play classification for triggering pre-recorded sound samples, in a "funk" style. • Grab-and-play regression for controlling audio effects (pitch shifting and reverb). • The same sample triggering and effects control as above, but using Wekinator's existing supervised learning interfaces for classification and regression (i.e., requiring users to specify labeled training examples). In each small group, the workshop leader controlled the computer (including the GUI for mapping creation), and the youth participant was given the input controller (sometimes with the help of parent/guardians). Participants therefore did not have to learn to use the GUI or other software. All participants tried at least one grab-and-play mapping, and participants who had time and expressed interest also tried supervised learning mapping.

Observational study

Grab-and-play setup

Our grab-and-play approach was very useful to build adapted interfaces. It allowed us to build instruments whose gestural range was wholly dependent on the participant: during the recording step, some people made wide movements, while others with strong motor disabilities were only able to make small movements. In this sense, the adaptivity of our tool prevented it from building non-playable instrument for a given person. Some participants also seemed to find the exploratory side of the running step very fun. They spent a lot of time trying to find where the different audio samples were in their gestural space: this activity seemed to capture participants' attention, as they usually seemed to engage in choosing which sample to trigger.

Grab-and-play classification seemed to elicit different types of interaction compared to regression. People using classification focused on triggering known sounds, whereas people using regression focused on exploration (alternating between searching for new sounds and playing them back). Both approaches thus have their own pros and cons, depending on which musical activity people and carers want to take part in.

Original Wekinator setup

Participants who had enough concentration also tried the supervised learning setup. They first recorded different GameTrak positions for each of the four classes of samples, and then tried their instrument. Several participants reported that they liked being able to choose where to place the audio samples in their gestural space, giving them even more control on what was going on. However, it was hard for some participants to concentrate on the process of choosing different gestures to trigger different samples. Even if the customization of the interface was enjoyed by some participants, it was not necessary to support meaningful musical experiences for most participants.

Both classification and regression were understood by participants, as they knew which audio effect to expect since they had chosen them during the recording step.

General discussion

This preliminary workshop has shown the utility of our grab-and-play approach to build custom musical interfaces. Our observations show this approach can be useful to build personalised devices, both for participants that were not able to concentrate for a long time, and for participants with specific motor disabilities. In any case, using the grab-and-play mapping could be a fun first musical experience for these young people. Supervised learning could later allow them to more deeply explore customisation.

These observations suggest improvements for our future workshops, in which we plan to experiment with other musical activities, and to test future grab-and-play implementations. Other input devices (such as joysticks, Wiimotes, or dance maps) as well as other output programs (such as instrument-specific samples or visual outputs) could be used to design instruments that are even better customised to each participant. Further, the social aspect of collective musical practices could be investigated through grab-andplay mapping, for example by having different young people exchanging their newly-created models, or more simply by having teachers sing with young people's sonic outputs.

INTERVIEWS WITH COMPOSERS AND PERFORMERS

We report on interviews held with three professional computer musicians to analyze how our grab-and-play approach could influence their music practice and/or composition processes.

Interview setup

Participants

We held individual interviews with three professional computer musicians. All three were composers and performers, as well as active in teaching computer music at university level. One reported previous experience with the Wekinator. We hoped to gather feedback to better understand how our grab-and-play approach could support embodied exploration processes and rapid mapping generation. We also aimed to collect information on ways to improve our first implementation. For instance, we wondered how much control the random generation method would leave to composers.

Structure

Each interview was a 30-minute exchange in which experimentation alternated with semi-structured interview questions. The musician was presented with a one-stringed Ga-meTrak which allows the sensing of a user's 3D hand position, while the first author controlled the computer GUI and led the interview. Experimentation started with our grab-and-play paradigm, spanning regression and classification algorithms; it ended with the original supervised learning setup, using the same regression and classification algorithms. When first trying the grab-and-play setup, composers were not told about its implementation: they thus had no presuppositions when experimenting with it.

They were asked about their playing strategies and how they thought it was working. Then, they were asked about ways they could imagine improving this grab-and-play approach. Finally, they used the original Wekinator supervised learning setup, allowing them to experiment and compare the two approaches. For regression, we used a digital synthesis instrument based on similarities between physical models of the flute and electric guitar [START_REF] Trueman | Percolate[END_REF], potentially allowing for vast sound space exploration. Experimentation with classification relied on the sample trigger we used in the previous user study.

Observational study

Grab-and-play setup

The exploratory aspect of our grab-and-play approach was praised by the three composers. One of them described the system as "kind of an enigma to solve", and was interested in the fact that "it kind of challenges you: you have to explore a bit, and try to understand the criteria, or how to deal with these criteria" to perform with it. Also, the possibility of rapidly prototyping a new instrument allowed them to experiment with very different gestural and sonic interactions. Using the same recorded gestural stream to build two instruments, one composer reported when comparing their playing that "[he doesn't] feel any consistency between them in terms of gesture and sound: they felt like completely different mappings", saying he could explore them "endlessly". Different strategies were adopted to exploit the system's capabilities. One composer first spent time exploring the sonic parameter space, then tried to regain control and to replicate certain sounds. He then decided to reduce the space he was exploring by moving the controller in a given plane rather than in 3D, allowing him to learn certain gestureto-sound relationships in a "pleasant" way. In this sense, one composer reported he could eventually learn how to play such an instrument. After having been told gestural data was randomly selected, one composer tried to exploit this aspect by spending more time in certain locations in his gestural space to increase the likelihood of their inclusion in the mapping. He indicated he was interested in "playing more" with this exploit.

The random selection also had some weaknesses: for example, a composer reported he had too little gestural space to explore between two interesting sounds in a given mapping. Another composer said he would require more control over the selection of sound parameters while agreeing that randomly selecting could "definitely" go with his vision of composing ("the embodiment of being able to control the sound with enough level of control, regardless of what the movement is"). Ways to modify a given mapping would be required as an improvement (this is discussed in section 5.2.3).

Original Wekinator setup

When testing the original Wekinator setup, one composer underlined its effect on his expectation of how a given instrument would work: "it sets up all the run expectations, and it also affects the way I play it, because now I start to remember these poses, rather than just exploring in an open-ended way". Choosing gestures when building a mapping can thus be a responsibility composers want to avoid when creating meaning through sound. In this sense, a composer even mentioned that he "never care[s] about gesture" in composition, rather seeing these gestures as movements that are related to his own instrument practice: "actually, what I care about is the exploration process afterwards". On the other hand, one composer liked the fact that he could immediately replicate a given sound as he "kind of see [s] what's being mapped there". He enjoyed the idea of spending less time on exploration and having more control, as "in some kind of performance, you want to be very meticulous". Comparing the grab-and-play and original Wekinator setups, composers seemed to agree that "both are useful", depending on what they would want to achieve. "If you set up the mapping yourself, and the system yourself, you have more control, but then again maybe it's too predictable", one composer summed up.

Suggestions for improvement

Talking about ways to improve such setups, one composer evoked the idea of "a hybrid approach", where one could record specific gesture-sound relationships and add some randomness in between: "some points could be manually controlled, and some points automatically". This would be a way to address the previously-mentioned trade-off between control and exploration: one could then explore and discover the control space during performance, while having access to predetermined gesture-sound relationships in the mapping.

The random selection was praised for its rapidity in prototyping and experimenting, as for "most trainings, actually, you're not really so concerned about the specific thing that's done: you just want stuff mapped out". However, composers would like to have a bit more control over both gesture and sound when building such a mapping. In this sense, one could imagine clever ways to select gestural and sound parameters that would still enable rapid instrument prototyping. Going further, one composer suggested incorporating the design process within the performance. Instead of being "a static thing", the design process would become a real-time evolution of one's control space ("me creating the control space in real-time"). For example, such a performance could entail repeating the same gesture to tell the machine to add new sounds to this gesture. This idea is reminiscent of Fiebrink's play-along mapping approach [START_REF] Fiebrink | Play-along mapping of musical controllers[END_REF].

Finally, one composer noticed the difficulty in editing a newly-generated mapping: "It's really frustrating when you're working musically because you just want to tweak that thing, and then the whole thing blows up". One could edit the training data, or, as the composer suggested, "regression is just a geometry, so why can't we just start stretching things and manipulate them?" Designing a user interface that allows the intuitive modification of an N-dimensional geometry would be necessary; however, this goes beyond the scope of our grab-and-play mapping paradigm.

General discussion

These individual interviews have clarified what kind of compositional processes could be allowed with our grab-andplay approach. Composers' opinions globally corresponded to our intuitions about the discovery and exploration processes encouraged by our first implementation of the tool.

As mentioned by one composer, such a random process may be used when starting a piece, as a way to let new ideas emerge, then opening up a reflection on how to use them: quoting him, "all these mapping processes are about making decisions that are rational: it's just building blocks. Then, musical decisions come as you actually walk through them..."

Other implementations of our grab-and-play paradigm may also support composers' needs (see Figure 2). For example, clustering gestural data could meet composers' need for control over their gestural space in relation to sound, while allowing rapid prototyping. This setup is already implemented but not yet tested. Also, most composers wanted to have more control over the choice of sounds: in future work, we would like to allow a user to choose output labels by selecting high-level perceptual characteristics of a synthesis engine's sound space. Finally, hybridizing grab-and-plau mapping with the original supervised learning setup could be a way to encourage discovery while allowing customization. We plan to experiment with each of these implementations in the near future.

CONCLUSIONS AND FUTURE WORK

We presented a first implementation of our "grab-and-play" approach to mapping that allows the prototyping of digital musical instruments. We reported on a first workshop with disabled young people, suggesting that the tool could be useful in the context of musical inclusion. The rapid prototyping of adapted musical interfaces allowed youth with less concentration to instantaneously take part in musical activities, while those with more concentration were curious about both grab-and-play and supervised learning setups, notably enjoying the customization of the latter. We also reported on interviews with three composers and performers, suggesting that the tool could encourage the realisation of new musical outcomes. Each of them valued the grab-and-play approach for embodied musical exploration, and underlined the balance between discovery and control that such a paradigm could support. Their feedback allowed us to imagine future improvements to the current implementation. More generally, the grab-and-play's simple yet expressive framework reflects our wish to get more people progressively included in modern musical activities, and in a broader sense, to have them create new technologies more easily.

In the next two years we will develop our contribution to musical inclusion through workshops and prototypes that will implement more engaging musical activities that are specifically adapted to a participant's abilities. We are also currently implementing more sophisticated ways to select gestural inputs and sound outputs. Using unsupervised learning algorithms to extract relevant clusters from the recorded gestural stream could be a possibility. Another possibility would be to generate input data that are more equally spread through the space delimited by user's gestural extrema. The choice of output labels could also be informed by the relationship between synthesis parameters and higher-level perceptual characteristics, enabling the creation of instruments capable of accessing a desired perceptual sound space. Hybrid approaches mixing graband-play mapping with user-provided pairs of inputs and outputs could also be a way to encourage exploration while allowing customization. More generally, we believe that having digital musical instruments generate their own gestural interactions just as they generate sounds could be an engaging conceptual framework, both scientifically and artistically, as it remains mostly unexplored in the context of computer music.

Figure 1 .

 1 Figure 1. Our first implementation of the grab-and-play mapping paradigm. Inputs and outputs are respectively drawn from the user's recorded gestural stream and the sound parameter space. Outputs from 1 to N are sound synthesis parameters. In this schema, the training database contains two examples (i.e. two input-output pairs).

Figure 2 .

 2 Figure2. The current GUI of the tool. Observational studies reported in this paper only used the random implementation for input; random implementation and preset implementation for output. Other implementations of the grab-and-play approach are already implemented (see section 6), and will be studied in the near future.

  • The GUI allows users to easily change mappings by deleting and adding training examples. • Advanced or curious users can customise aspects of the machine learning process, e.g., changing the learning algorithm or its parameters, changing the selected features, etc. • Learning algorithms are set default configurations that have been shown to work well for many map-2 http://github.com/hugoscurto/GrabAndPlayWeki

http://www.nmpat.co.uk/music-education-hub/Pages/musicalinclusion-programme.aspx

Acknowledgments

We thank Simon Steptoe and the Northampton Music and Performing Arts Trust for inviting us to join their Musical Inclusion Programme (funded by Youth Music and the Paul Hamlyn Foundation). We also thank Simon Katan, Mick Grierson and Jérémie Garcia for useful discussions and suggestions.