In a Silent Way
Communication Between AI andImprovising Musicians Beyond Sound
MASAHIRO ISERISTUDENT NUMBER：46193003

InformationTitle

In a Silent Way: Communication Between AI andImprovising Musicians Beyond SoundAuthor

McCormack, J., Gifford, T., Hutchings, P., Llano
Rodriguez, M. T., Yee-King, M., & d'Inverno, M.

Conference

CHI2019

Keyword

AI Systems, Improvisation,
Extra-musical Communication

Background (1/2)
As interaction with creative AI becomes more commonplace,
how we collaborate with AI systems is important
Collaboration is built a trust, and many factors have been identified
as significant to increasing trust in human-computer interaction:

Reliability, Predictability, Utility, Provability, Transparency, …

Background (2/2)
Author is interested in revealing the state of human-machinecollaboration
Improvisation session uses many extra-musical cues to expose theirmental or emotional states→ Investigate the benefits of extra-musical interaction in real timemusic improvisation

Implementation
Using Temporal Convolutional Neural Network (TCN), implement amachine improviser
Human instrumentalist and machine improviser communicates with
their inner state

Human: biometrics (skin conductance)Machine: confidenceExperiment
To evaluate machine improviser and musical output,
Two experiments has conducted

1. Performer Evaluation
2. Listener Evaluation

Performer Evaluation
7 human instrumentalists improvised with machine improviser

Machine improviser visualize its inner state in three ways:
Truthful, Absent, DeceptivePerformer Evaluation - Result

Listener Evaluation
100 listeners compare three sets of improvised tracks:
Truthful vs. Deceptive

Two questionnaire‘Which performance was more interesting?’‘Which performance had a better musical balance betweendrums and saxophone?’

Listener Evaluation - Result

Conclusion
Investigating the influence of extra-musical communication onhuman-computer musical interaction
Visualizing Confidence affected the tendency of the instrumentalist

The biometric communication did not make any difference→Explore other modes of extra-musical communication

The Role of Physical
Props in VR Climbing
Environments

Peter Schulz
Dmitry Alexandrovsky
Felix Putze
Rainer Malaka
Johannes Schöning
University of Bremenクライミングでは落下の恐れを克服することが重要

Lew Hardy and Andrew Hutchinson. 2007. Effects of Performance Anxiety on Effort and Performance in Rock Climbing: A Test of Processing Efficiency Theory. Anxiety, Stress, & Coping 20, 2, 147–161.

恐怖症を克服する”Golden Standard”の1つは暴露療法

Mark B. Powers and Paul M. G. Emmelkamp. 2008. Virtual Reality Exposure Therapy for Anxiety Disorders: A Meta-Analysis. Journal of Anxiety Disorders 22, 3 , 561–569.

クライミングの高所恐怖症におけるVRETに身体感覚がどの程度必要かを調査

3条件の比較実験

Creal ：実際のクライミング
Cprops：VR空間を視野として提示
Cctrl ：ゲームコントローラーを使用した仮想クライミング

Creal：実際のクライミング

https://www.gravity-research.jp/freeclimbing/toprope_climbing/Cprops：VR空間を視野として提示

結果(代表的なものを紹介)

結論

VR環境は落下の恐怖を克服するなど、クライミングを訓練するのに役立つツールであると仮定できる

議論

複雑なコースや課題への対応がこれからの課題

ZeRONE: Safety Drone withBlade-Free Propulsion

情報理工学コース
46193023
佐藤拓斗

論文の位置づけ
Human-Drone Interaction (HDI)
従来物流
商業

今後

軍事
建設
人とドローンがより密に関わる↓屋内外の公共施設で利活用
センシング
ジェスチャーインプット

人とドローンの適切な距離が必要

研究背景
目的
公共施設で利活用のできるドローンの開発

従来ドローンの課題
安全性

プロペラの接触事故/ドローンの落下

静音性

プロペラが発生する騒音による快適性の低下

飛行時間

一般的に最大20分間と短い飛行時間

提案手法
ZeRONE :プロペラフリー飛行船型ドローン
機体 :アルミフィルムを用いたヘリウムガスバルーン型
推進力 :
圧電素子の超音波振動を活用したマイクロブロア

提案手法• プロトタイプの実装✓4個のマイクロブロアで構成された推進力モジュールを機体の左右に3個ずつ設置✓機体下部にバッテリーと制御回路
移動方向と操作するブロア・前進・上下移動
・ヨー角方向の回転

評価実験
運動性能

上下方向の移動速度

ヨー角方向の回転速度• 最大移動速度(上下運動) : 20cm/s• 最大回転速度(20秒経過) : 80°/s

評価実験
ノイズレベル• 最大ノイズ(1m) : 57.7dBA↓
従来よりも大幅に静寂(従来ドローン(8m) : 80dBA)
機体周辺のノイズレベル

飛行時間• 約30分間制御可能• 約2週間浮遊可能 (バッテリー切れによる制御不能後)

まとめ
ZeRONE利点 :安全性と静寂性に優れた長時間飛行が可能なドローン
欠点 :推進力が弱く、慣性・風の影響を受けやすい活用案

SottoVoce: An UltrasoundImaging-Based Silent
Speech Interaction Using
Deep Neural Networks
宮坂清貴

Background• The availability of digital devices operated by voice
is expandingspeech recognition• Problems• Cannot be used in public places• Cannot be used in a noisy environment• Not confidential• Solution• No voice speech recognition

Method• Ultrasound Imaging-Based Silent Speech Interaction

SottoVoce system overview

Network2

Training• 500 speech commands• two collaborators for data
• Training Network 1 required approximately 4 h• Training Network 2 required approximately 1 hTest• four commands• Alexa, play music• Alexa, what’s the weather like
• Alexa, what time is it• Alexa, play jazzProblem• Slow recognition(2.61 s)• Low recognition rate
• Sound is hard to hear
• Few commands

“At Your Service: Designing Voice Assistant
Personalities to Improve Automotive User
Interfaces: A Real World Driving Study”
Michael Braun, Anja Mainz, Ronee Chadow itz,
Bastian Pfleging, Florian Alt

46193175 Koki Ebina

Outline
1.Introduction
2.Characterize
3.Real world driving study
4.Result
5.Conclusion
2

Introduction
 Voice assistants are becoming a pervasive meansof inter action in automotive UIs
 Voice assistants offer:


Minimizing driver distraction during manual driving
More natural user experience (UX) Current voice assistant can:

Understand natural language
Express information through speech synthesis

Most of them lack an inter personal level of communication

Satisfying the expectations of users havetowards social interaction is needed
3Introduction
Personalized voice assistants may affect trust, UX,
acceptance and workload in the real world

However, it is so far unclear how affect
Designing a set of personalized voice assistants
and tested them in a real-world driving study

Objective

Evaluating the affect of personalized voice assistantson these factors compare to non-personalized voiceassistants4

CharacterizePre-study

 Subjects:
N=19 (12 male, 7 female, 19-53 years)
 Procedure:
Experiencing 6 scenarios with 8 voice assistant, adding up to48 total interactions
 Results of questionnaires: Assistants with a perceived friendly attitude were liked
 Unfriendly behavior and excessive talking were identified
as negative traits
Distance between assistant and user
The balance of power within the conversations
are considered as an important aspect to be felt as friendly 5Characterize
From the feedback of pre-study: Hostile assistant were removed
 Introducing the dimension “professionalism”(which defines the level of casual or formal behavior)

Final charactersFig. 1: The models of personalized voice assistant6

Real world driving study
 Subjects:
N=55 (45 male, 10 female, 23-60 years). They answered thequestionnaire to select a fitting assistant in advance Procedures: The subjects drove a car and experienced interaction
with voice assistant.
 The operators sitting in the back of driver and triggered
the use cases in appropriate situations

Speaker (output ofthe voice assistant)
The operator (back seat)Fig. 2: The experiment environment
inside the car and driving route7

Real world driving study Procedures:
 Each subjects experienced two ride
(With recommended assistant and with default assistant) 12 use cases were triggered (which can split into 3 clusters:
Driving related, Proactive assistant, and connected car)
 Evaluation:
After each use case
 Rating the interaction verbally (good, neutral, bad)
After the ride
 Answering the questionnaires, and giving feedback for theexperienced character Listening all 5 characters and decided which characters theywould like to use in the future
8Result 4 characters assigned to subjects by the deciding tree from theresult of the questionnaires
FriendButler

AuntAdmirer

2116

15

3*

* The data of admirer was excluded from the analysis because low number of subjects

 Subjects were divided into 2 groups based on the result of thequestionnaires
Correct matching : who chose suggested personalizedcharacters (N=16)
Incorrect matching: who chose other characters (N=39)
 About Trust, Likability, Usefulness, and satisfaction wereevaluated by a 7 point evaluation (-3 to +3)
9ResultFig. 3: The result of the T-test about
the trust (*p<0.05, **:p<0.01)

Fig. 4: The result of the T-test about
the likability (*p<0.05, **:p<0.01)

 Correct matching group: the scores of trust and likability arehigher than that of default characters Incorrect matching group: the scores of trust and likability arehigher than that of personalized characters
10Result

Fig. 5: The result of the T-test about
the usefulness (*p<0.05, **:p<0.01)

Fig. 6: The result of the T-test about
the satisfaction (*p<0.05, **:p<0.01)

 Correct matching group: the scores of usefulness andsatisfaction are same as that of default characters Incorrect matching group: the scores of usefulness andsatisfaction are higher than that of personalized characters

11Conclusion
 If the voice assistant matches the user’s personality,personalization has a positive effect on trust and likability
 Mismatch cause displeasure, and in the case, default characters
were preferred.

A neutral assistant is recommended as starting
point before gradually adjusting its personality
to the user’s needs

12Thank you for your kind attention

13