LEVERAGING CULTURAL ASSETS OF CULTURALLY AND LINGUISTICALLY DIVERSE INDIVIDUALS IN MUSIC THERAPY: A QUALITATIVE PHENOMENOLOGICAL STUDY

The purpose of this thesis was to explore how music therapists can engage in cultural humility and leverage cultural assets of those who are culturally and linguistically diverse (CLD). The primary research question involved in this study was: “In what ways can music therapists leverage cultural assets of CLD individuals in practice?” Sub-questions were: “What awareness, knowledge, and skills do music therapists need to grow in culturally sensitive practice?” “How can music therapists whose first language is English effectively serve linguistically diverse individuals?” “What steps might music therapists take to effectively serve culturally diverse individuals?” and “How might cultural and linguistic diversity influence the therapeutic relationship?” I investigated these questions using a qualitative phenomenological study. Specifically, I conducted semi-structured interviews with a purposive sample of eight experts including music therapists, specialists in Universal Design for Learning (UDL), and specialists in diversity, equity, and inclusion. I analyzed and interpreted the findings from these interviews using a combined inductive and deductive qualitative data analysis approach. Throughout this study, I used the frameworks of UDL and the Multicultural and Social Justice Counseling Competencies to inform research and interview questions, analysis, and interpretation of results. Findings of this study provide foundation and practical guidance for music therapists regarding inclusive, equitable, and culturally sensitive clinical practices with CLD individuals

Toward Interactive Music Generation: A Position Paper

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations

From Words to Music: A Study of Subword Tokenization Techniques in Symbolic Music Generation

Subword tokenization has been widely successful in text-based natural language processing (NLP) tasks with Transformer-based models. As Transformer models become increasingly popular in symbolic music-related studies, it is imperative to investigate the efficacy of subword tokenization in the symbolic music domain. In this paper, we explore subword tokenization techniques, such as byte-pair encoding (BPE), in symbolic music generation and its impact on the overall structure of generated songs. Our experiments are based on three types of MIDI datasets: single track-melody only, multi-track with a single instrument, and multi-track and multi-instrument. We apply subword tokenization on post-musical tokenization schemes and find that it enables the generation of longer songs at the same time and improves the overall structure of the generated music in terms of objective metrics like structure indicator (SI), Pitch Class Entropy, etc. We also compare two subword tokenization methods, BPE and Unigram, and observe that both methods lead to consistent improvements. Our study suggests that subword tokenization is a promising technique for symbolic music generation and may have broader implications for music composition, particularly in cases involving complex data such as multi-track songs

Leveraging Explanations in Interactive Machine Learning: An Overview

Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once users understand, they can then provide feedback. The goal of this paper is to present an overview of research where explanations are combined with interactive capabilities as a mean to learn new models from scratch and to edit and debug existing ones. To this end, we draw a conceptual map of the state-of-the-art, grouping relevant approaches based on their intended purpose and on how they structure the interaction, highlighting similarities and differences between them. We also discuss open research issues and outline possible directions forward, with the hope of spurring further research on this blooming research topic