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MR4MR: Mixed Reality for Melody Reincarnation

Atsuya Kobayashi
Graduate School of Media and Governance
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected] \ANDRyogo Ishino
Faculty of Environment and Information Studies
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected] \AndRyuku Nobusue
Graduate School of Media and Governance
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected] \AndTakumi Inoue
Faculty of Policy Management
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected] \AndKeisuke Okazaki
Faculty of Environment and Information Studies
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected] \AndShoma Sawa
Faculty of Environment and Information Studies
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected] \AndNao Tokui
Graduate School of Media and Governance
Keio University
5322 Endo, Fujisawa City, Kanagawa Japan
[email protected]
Abstract

There is a long history of an effort made to explore musical elements with the entities and spaces around us, such as musique concrète and ambient music. In the context of computer music and digital art, interactive experiences that concentrate on the surrounding objects and physical spaces have also been designed. In recent years, with the development and popularization of devices, an increasing number of works have been designed in Extended Reality to create such musical experiences. In this paper, we describe MR4MR, a sound installation work that allows users to experience melodies produced from interactions with their surrounding space in the context of Mixed Reality (MR). Using HoloLens, an MR head-mounted display, users can bump virtual objects that emit sound against real objects in their surroundings. Then, by continuously creating a melody following the sound made by the object and re-generating randomly and gradually changing melody using music generation machine learning models, users can feel their ambient melody "reincarnating".

1 Introduction

In computer music and digital artworks, many interactive experiences that focus on the objects and physical space around us have been designed so far. In the past few years, XR (Extended Reality) environments have also become easily accessible, and this has led to the development of the research field of Musical XR [1]. Mixed Reality (MR) with head-mounted displays can be used for spatial music experiences since it can provide the user an immersive experience like Virtual Reality (VR), while interacting with the real space like Augmented Reality (AR).

Exploring musical elements with the entities and space around us has been challenged for a long time, such as musique concrète and ambient music. In the context of computer music and digital art, interactive experiences that concentrate on the spatial environment: surrounding objects and physical spaces have also been designed. In the context of MR, which can handle human physical movements in virtual space, various systems of musical experience have been proposed for both listening and composing music. As for the design of music listening, an interface for surround music listening is proposed, that allows the user to design a free surround space in an MR environment by freely arranging sound sources in the real space [2]. And a musical mobile application is proposed that added a spatial dimension to a music mobile application to allow users to listen to a song as if it were played live [3]. On the other hand, in the context of composition and performance support, Music Everywhere [4] proposes an interface to support performance practice from a virtual display on top of the real keyboard in MR. MiXR [5] is also an MR-based system that provides musical instrument players with controllable musical score sheets presentation. Besides, in the field of sound art, Sonic Sculpture [6] proposes to extend the audience’s approach to sound in the experience of sound sculpture by wearing a head-mounted display. Brian Eno has designed an experience that creates sound art from spatial interactions [7]. This includes a synthesizer interface that creates ambient music from curves drawn by the user, and a system that allows multiple people to experience audiovisual effects from mid-air drawing line interaction [8]. Multimodal connectoR [9] is an application that users can experience sounds that match the collision position of virtual objects in the MR space. A library OSC-XR [10] has been proposed for the design of music interfaces on XR as described above. There are also actual contents of music experiences with XR provided as services: such as Drops: Rhythm Garden111Drops: Rhythm garden https://store.steampowered.com/app/864960/Drops_Rhythm_Garden/, which is a game to enjoy the sound of objects’ movements in VR space, and Fields222Fields, Planeta.Cc https://fields.planeta.cc/, which offers the experience of creating music from the surrounding environment. Some companies design musical experiences and tools for the XR environment333Emotionwave XR https://emotionwave.com/xr/444PatchXR - Sound of the Metaverse https://patchxr.com/. XR is very useful as a platform for designing new musical experiences and will be further developed in the future.

In the area of musical artificial intelligence, fairly sophisticated interpretation and generation have become possible and are beginning to be used for recommendation and composition support respectively; Google Magenta offers Magenta Studio555Magenta Studio. Magenta. https://magenta.tensorflow.org/studio/as a tool, which applies MusicVAE model [11] for music production, and proposes an attempt to extend a number of musical experiences as interactive demos. Adapting automatic composition algorithms to the design of new musical interactions could provide novel musical experiences. As an example of an application to audibility, [12] applies MusicVAE to the conversion of data flows into sound in Dublin’s Smart City, proposing a way to utilize music generation models beyond composition support. In addition, Malakai [13], a tool that covers the process of remixing, uses MusicVAE to show the flow of music composition in real-time to accompany an interactive experience, showing the potential of applying artificial intelligence to musical experiences outside the context of normal DAW composition. A review on artificial intelligence and XR [14] analyzes machine learning approaches on many XRs, but no research cases on music are presented. As mentioned above, music artificial intelligence can be a method to create various sound experiences from a wide variety of interactions, so we design an experience that generates Background Music from spatial interactions.

In this paper, we propose the interactive system MR4MR, which realizes the novel experience of listening to the music generated from the user’s interaction with surrounding physical spaces. With MR4MR, users can give birth to a new melody by touching virtual objects floating in the MR space, and listening to the gradual change, which we called "Reincarnation", of the melody once created by the user. In the following sections, we describe the detail design / implementation of the application, evaluation of the experience through feedback in installation exhibition, and discussion about the technical limit and future works. A demo video is available at https://youtu.be/xPMbqHUlyyU

2 Usage and System Design

To experience MR4MR, the user puts on the HoloLens head-mounted display and wireless headphones and launches the application on the MR field. Three kinds of 3D objects are spawned at the starting point, and the user can pick up the balls, move them, or hit them to make them glide. When these objects collide with walls or objects in real space, such as chairs, tables, laptops, etc., they generate a collision sound and bounce back. We defined gravity as well in the virtual space, and once the user touches the object, it falls to the floor and starts bouncing. This force of gravity can be modified in the settings and is also changed when a particular object collides (Figure 1).

Refer to caption
Figure 1: Fig (a) shows the MR4MR being experienced in the computer room, and the view is shown in Fig (b). Fig (c) shows a corridor in a building, and the view from the player is as shown in Fig (d).

Suppose some collision sounds get played more than a certain number of times within a given period. Then, we treat them as a beginning of a base melody piece and feed them into generative melody models to generate the following melody (Figure 2). Once the generated melody plays to the end, it will loop, but the melody will change slightly every few bars by the method described later. In addition, the scale of the melody changes depending on the ambiance of the place where the object hits, so users can enjoy the changing background tune as they reposition from one spot to another, such as a dark place, a bright room, or a room with colorful and vividly colored walls.

Refer to caption
Figure 2: Generative models create the sequence of notes that follows the input base melody.

This system consists of 4 modules.

These modules are linked via OSC (Open Sound Control999Open Sound Control, https://opensoundcontrol.stanford.edu/) (Figure 3). Modules other than the MR interface can be run on both a single computer and multiple computers connected to the same network.

Refer to caption
Figure 3: System Architecture: MR4MR application consists of 4 modules developed with Max, Python, and Unity. Each module has sub-modules separated to handle specific functions and sub-modules connected via OSC to each other to communicate with other modules.

2.1 Mixed Reality Interface

Using Microsoft’s Mixed Reality Toolkit101010Mixed Reality Toolkit https://github.com/microsoft/MixedRealityToolkit-Unity, which delivers a set of features for inventing interactive scenes with HoloLens, we designed 3 types of interactive virtual 3D objects in Unity111111Unity Real-Time Development Platform — 3D, 2D VR AR Engine, https://unity.com/. Each of the 3 types has a distinguishable collision sound, and one of them randomly changes the gravity setting in the MR environment when it collides. Information of type, velocity, and coordinates of each object are sent to the melody generation module when the object collides with a 3D mesh surface generated on a wall or the surface of an object in real space.

Objects in real space, such as walls and desks, are also recognized in real-time by the field view camera mounted on the HoloLens, and a 3D mesh is generated along with them, so virtual objects can bounce and fly when they collide with real objects. Consequently, if there are many objects in the space where the user plays MR4MR, the number of sounds will increase, and following melodies will be generated incessantly.

2.2 Melody Sound Module

Users can design the atmosphere of collision sounds since they can edit the mapping of timbre and each object in Ableton Live if they want. A sound sample of a bell, xylophone, or glockenspiel is assigned by default. Collision sound has a pitch based on the height of the collision location and a volume according to the collision velocity. Using the Oculus Spatializer VST plugin121212Oculus VST Spatializer https://developer.oculus.com/documentation/native/audio-osp-vst/, collision sound feedback is processed to surround sound to enrich the fidelity and enable the user to hear collision sounds from a precise location.

As a implementation of this function, every received object information consisting of the coordinates of the user and the coordinates and speed of the object is converted into a MIDI note and Control Change value. The module consist of a Max patch and a Max for Live device respectively, each with the sub-modules described in Fig.3. The patch has sub-modules of Note Receiver and Pan Controller for receiving the information via OSC and send spatial information as MIDI Control Change to Oculus Spatializer Plugin. Max for Live device has sub-modules of MIDI inserter and Trigger Controller to receive generated melody MIDI note sequence via OSC and create MIDI clip on Ableton Live session view via Live API.

2.3 Melody Generation Module

In MR4MR, the user not only hears the collision sounds but also listens to the tune of automatically generated melody that follows the tune of collision sounds. When more than a certain number of collision sounds occur in a certain period, the system regards the collision sound sequence as a base melody component and generates the following part of melody with a trained MelodyRNN [15] model. Generated melody piece is played in the loop with an instrument assigned in Ableton Live. As the generated melody is played back, notes information is sent back to the HoloLens for visual feedback. While playing the generated melody, a blue wave-like graphic is projected into MR space to show the user from which height collision the melody was generated.

If the generated melody is looped more than a certain number of times, a slightly modified melody is generated and overwrites the currently looped melody. The new melody is reconstructed from the sum of random noise and latent vector got by encoding the previous melody into a public trained 2-bar MusicVAE [16] model. In this model, a bar of monophonic melodies in sequences of 16th note events are used. Approximately 1.5 million MIDI files collected from web were used to train the model. If there are fewer objects in the physical space of the MR4MR experience or fewer interactions with virtual objects, the user can enjoy the intermittent, gradually changing flow of a spawned melody. The continuous modification of the loop melody is achieved by adding a random noise to the latent vector zz encoded from previous loop to generate the next loop in every two bars. Random noise is a vector with the same size as zz and each element sampled from a uniform distribution of min 0 and max 1.041.0^{-4}.

2.4 Field of Vision Module

Refer to caption
Figure 4: Getting HSV color data from a screenshot image of the field of view camera mounted on HoloLens.

By extracting the color information, this system converts the generated melody scale to match the mood of the space. The image of the HoloLens’ field of view is captured once every few seconds via web interface pre-built in HoloLens. Each pixel of the acquired image is clustered by RGB value with the k-means algorithm (Figure 4). The color value that makes up the majority of the screen area is sent to the music generation module. Each key and scale will have its own distinctive emotional image, and each type of color and brightness will have its own associated impression, then the color information can be converted into keys and scales.

Alexander Scriabin proposed to correspond mappings of color and tone. Project Scriabin [17] is an interactive system of synesthetic experience. We developed a key and scale converter on the basis of the table (Table 1), which was formulated with reference to Scriabin’s color to scale mapping. In the previous work [18], the value of Hue was defined as a single scalar value for each tonic of the key. In this work, however, each boundary between colors was set to the value that is equidistant from the adjacent colors. In addition, based on the general impression that major tunes are brighter and minor tunes are darker, we set a threshold of Saturation and Value between major and minor scales.

Table 1: Color Range to Scale Mappings
Typical Color Hue Saturation Value Tonic Scale
Red [331, 360] [.0, .6] [.7, 1.0] C Major
Maroon Color [0, 19] [.0, .6] [.0, .7) C Minor
Light Orange [20, 49] [.7, 1.0] [.8, 1.0] G Major
Mustard Yellow [20, 49] [.3, 1.0] [.5, .8) G Minor
Canary Yellow [50, 90] [.0, .7) [.8, 1.0] D Major
Olive Color [50, 90] [.4, 1.0] [.2, .8) D Minor
Neon Green [91, 140] [.2, 1.0] [.8, 1.0] A Major
Dark Green [91, 140] [.5, 1.0] [.1, .8) A Minor
Aquamarine Color [141, 200] [.0, .3) [.8, 1.0] E Major
Teal Color [141, 200] [.2, 1.0] [.2, .7] E Minor
Blue [201, 248] [.2, 1.0] [.6, 1.0] B Major
Navy Blue [201, 248] [.2, .7] [.0, .6) B Minor
Blue Violet [249, 265] [.6, 1.0] [.7, 1.0] F# Major
Indigo Purple [249, 265] [.0, .7] [.0, .7) F# Minor
Heliotrope Purple [266, 277] [.0, .5) [.5, 1.0] C# Major
Koki Murasaki [266, 283] [.1, 1.0] [0., .5) C# Minor
Magenta Color [284, 310] [.4, 1.0] [.6, 1.0] G# Major
Byzantium Color [284, 310] [.1, 1.0] [.2, 6) G# Minor
Azalea Pink [311, 330] [.6, 1.0] [.2, 1.0] A# Major
English Violet [311, 330] [.1, .6) [.1, .4] A# Minor
Flamingo Pink [331, 350] [.2, .8] [.6, 1.0] F Major
Tyrian Purple [331, 350] [.2, 1.0] [.2, .7] F Minor

3 Evaluation

Table 2: Results of 5-point Likert scale questions
Question Result (mean)
Was the experience interesting? 4.67±0.73
How pleasant was your experience? 4.22±0.97
Was the experience is unique? 4.56±0.93
Do you think the experience was a musical experience? 4.22±0.80
Table 3: Feedback Comments from Participants
Participant No. Feedback Comment
4 The experience with Hololens was amazing!
8 The fusion of MR and music-generating AI was interesting!
9 I thought it would be fun to make the music more tangible if I could grab it!
14 I could not understand the concept of the work.
16 The mechanism of music generation was unique and interesting.
The experience of chasing floating sphere and cube was also fun,
like playing in the near future,
21 I thought it would be better to experience it in a space that separated from
the other space so that I could feel that I was tinkering with it by myself.
26 It would be more immersive if the refresh rate and response
speed were increased.

We created a system to experience music in the MR space, and in continuing the development, we conducted a questionnaire survey. The installation was exhibited at an exhibition in an art gallery (Figure 5), and a 5-point (1-5) scale questionnaire was given to 27 people. The results of the responses to the questions were generally high. (Table 2) In addition, we obtained optional open-ended feedback comments from participants (Table 3). There is no problem with the comfort of the operation through the basic experience and the technical novelty of this work was highly appreciated. But it is inferred from the last comment that there were issues with the instruction and care to convey the context of the artwork.

Refer to caption
Figure 5: Installation at an art gallery in Tokyo. On a desk in the exhibition space, for the benefit of non-experienced bystanders, displays are showing a view of the visitor and the Max interface that receives the collision sound.

4 Discussion

The contribution of this work is not only to provide people with a new experience, but also to propose a new approach to listening to music and making music under the Mixed Reality context, and to suggest new ways of using AI-based music generation algorithms other than to support music composition. However, at the moment, there are still many systemic issues that prevent us from achieving the ideal interaction. The opinion in the feedback comment that it would be better to be able to grasp an object with real haptics is challenging to realize with the current system unless using additional devices that can provide tangible feedback. Refresh rate and response time are also restricted by the hardware and are difficult to improve at present. If the performance of MR devices is improved, it will be possible to provide a better, more immersive experience.

In the real world, the sounds emitted by the objects around us depend on the material and weight of the objects. For example, the sound of a collision between a virtual object and a glass on a desk will be different from that of a collision between a wall and a glass on a desk. Therefore, as a future challenge and idea, if we can grasp what kind of real objects exist around us and where they are located by running the object recognition model with the MR device camera input, we can determine the type of sound output and have variations of collision sounds. In addition, the collisions between objects in our daily life are of objects such as cups and pens, not spheres and cubes. To simplify the operation, we used primitive 3D models to make the boundary between the virtual and the real easier to understand. By preparing 3D models of everyday objects such as mugs, pens, and remote controls, as well as broken objects that we cannot throw even if we wanted to, we expect to be able to create music from interactions that are closer to the real world.

The design goal of this work should be to promote an awareness of the musical potential of the environment around us through the experience of creating music specific to a place, where the characteristics of the music produced vary according to the space. To achieve this, it is necessary to actively design exhibition spaces with different characteristics, such as brightness and abundance of objects. Ultimately, we would like to design a system that allows people to move between multiple rooms or allows whoever has HoloLens to experience the system remotely via the Internet.

5 Conclusion

In this study, we implemented an interface that continuously generates music from virtual object interactions with objects in real space using MR devices. By designing a new experience of assembling melodies directly from the sounds of virtual objects around us, we proposed the new application of automatic music generation technology based on machine learning and an active way of interacting with background music specific to the place. There are still technical and software design issues to be resolved, so we plan to improve it to realize better interaction and musical experience.

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