Music in the AI era : 15th International Symposium, CMMR 2021, virtual event, November 15-19, 2021, Revised selected papers / Mitsuko Aramaki, Keiji Hirata, Tetsuro Kitahara, Richard Kronland-Martinet, Sølvi Ystad, editors.

Intro -- Preface -- Organization -- Contents -- Music Technology in the IA Era -- KANSEI Informatics and Music Technology in AI Era -- 1 Introduction -- 2 New Trend of Technology Development -- 3 KANSEI Informatics -- 4 AI and Music Technology -- 5 Conclusions -- References -- On Parallelism in Music and Language: A Perspective from Symbol Emergence Systems Based on Probabilistic Generative Models -- 1 Introduction -- 2 Language Acquisition and Music Composition Using PGMs -- 2.1 Multimodal Concept Formation and Lexical Acquisition in Robotics -- 2.2 Automatic Music Composition in Computers

3 Symbol Emergence Systems and Emergence of Semiotic Meanings -- 3.1 Symbol Emergence Systems -- 3.2 Collective Predictive Coding -- 4 On Parallelism in Music and Language -- 4.1 Parallelism on Syntax, Brain and Evolution -- 4.2 Symbol Emergence Systems on Music, Emotion, and Interoception -- 5 Conclusion -- References -- WaVAEtable Synthesis -- 1 Introduction -- 1.1 Motivation and Project Overview -- 1.2 Related Methods -- 2 WaVAEtable Synthesis -- 2.1 Sample Neural Network Architecture and Training -- 2.2 Wavetable Generation -- 2.3 Synthesizer Implementations

2.4 Incorporation of Existing Timbral Autoencoders -- 3 Future Work and Conclusion -- References -- Deep Learning-Based Music Instrument Recognition: Exploring Learned Feature Representations -- 1 Introduction -- 2 Attention-Based Model -- 2.1 Input Pre-processing -- 2.2 1st Stage: Post-processing and Representation -- 2.3 2nd Stage: CNN, Attention Mechanism and Classification -- 3 Datasets -- 4 Experimental Procedure -- 4.1 1st Stage: Pre-Training Objectives -- 4.2 2nd Stage: Downstream Instrument Recognition -- 5 Evaluation -- 6 Results and Discussion

6.1 Representation Post-processing: Impact on Performance -- 6.2 Learned Representations: Impact on Performance -- 7 Conclusions -- References -- Time-Span Tree Leveled by Duration of Time-Span -- 1 Introduction -- 2 Implementation Problems of Melodic-Morphing Algorithm -- 2.1 Ideas of Melodic Morphing -- 2.2 Partial Melody Reduction -- 2.3 Combining Two Melodies -- 2.4 Implementation Problems of Melodic-Morphing Algorithm -- 3 Implementation Problems of Deep-learning-based Time-Span Tree Analyzer -- 4 Solution: Time-Span Tree Leveled by Duration of Time Span -- 4.1 Automatic Melodic Morphing

4.2 Automatic Time-Span Tree Analysis by Deep Learning -- 5 Experiment and Results -- 5.1 Automating Melodic Morphing by Prioritization of Branches -- 5.2 Comparison of Seq2Seq and Transformer in Stepwise Time-Span Reduction -- 6 Conclusion -- References -- Evaluating AI as an Assisting Tool to Create Electronic Dance Music -- 1 Introduction -- 2 Related Work -- 3 Environment Setup and AI-Created Music -- 4 Test Structure and Efficiency Evaluation -- 4.1 Structure -- 4.2 Evaluation -- 5 Listening Evaluation -- 6 Summary and Conclusion -- References -- Interactive Systems for Music

Suiview: A Web-Based Application that Enables Users to Practice Wind Instrument Performance

This book constitutes the refereed proceedings and revised selected papers from the 15th International Symposium on Music in the AI Era, CMMR 2021, which took place during November 1519, 2021 as a virtual event. The 24 full papers included in this book were carefully reviewed and selected from 48 submissions. The papers are grouped in thematical sessions on Music technology in the IA era; Interactive systems for music; Music Information Retrieval and Modeling; and Music and Performance Analysis.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed June 27, 2023).