RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction

RoboJam is a machine-learning system for generating music that assists users of a touchscreen music app by performing responses to their short improvisations. This system uses a recurrent artificial neural network to generate sequences of touchscreen interactions and absolute timings, rather than high-level musical notes. To accomplish this, RoboJam's network uses a mixture density layer to predict appropriate touch interaction locations in space and time. In this paper, we describe the design and implementation of RoboJam's network and how it has been integrated into a touchscreen music app. A preliminary evaluation analyses the system in terms of training, musical generation and user interaction

Eliminating Fear and Unleashing Creativity: Incorporating Improvisation into Performance Practice and Education

abstract: This research project was written simultaneously with a composition for double bass and piano that centers around improvisational concepts. The composition is intended for intermediate to advanced musicians to have an opportunity to practice improvisational performance and, hopefully, further their understanding and improve their ability to make convincing and creative musical decisions. Improvisation, an aspect of music that has a deep tradition in Western Classical music, is often feared by classical musicians. The lack of improvisation in classical music, the idea that it is a specialized skill, and the lack of encouragement from studio teachers contributes greatly to this fear. In addition, teachers themselves often fear teaching and utilizing improvisation in performance for these same reasons. The introduction of improvisation into both the student’s and the teacher’s studies and daily practice can be beneficial in the development of meaningful performance and understanding music theory concepts. This paper will introduce improvisation into daily practice that will educate both the student and the teacher and cement the understanding of theoretical concepts and standard repertoire. Various improvisation games (creating new material and improvising from traditional classical music) will be introduced. This study will begin with a brief survey of the tradition of improvisation in Western classical music from the Middle Ages to the present.Dissertation/ThesisDoctoral Dissertation Music 201

The Role of Music in Personal Therapy in Advanced Music Therapy Training: A Self-Inquiry

The purpose of this study is to explore the role of music through the similarities and differences of an Analytical Music Therapy (AMT) and Nordoff-Robbins Music Therapy (NRMT) trainee. The study employed a first-person research design and thematic analysis to assess three AMT and three NRMT personal therapy sessions. Music as well as the verbal interactions during the music therapy sessions were transcribed and analyzed. Five themes were constructed from the data, three similar themes and two different themes: (1) music as a place of meeting and the foundation of the therapeutic relationship, (2) using the voice to strengthen the therapeutic and musical relationship, (3) music making and meaning making, (4) collaboration in music making, (5) music as a referential meaning or an experience. The study indicated that the role of music in AMT and NRMT had many purposes for the researcher, such as, a representation of emotional content for the client. There were also differences between the role of music for AMT and NRMT, in which the therapeutic themes were addressed in different ways. The findings of this study bring up questions about integral thinking in music therapy practice as well as understanding the theoretical basis and traditional models of AMT and NRMT with an emphasis on the role of music. The data also indicates benefits of personal therapy as an advanced music therapy trainee. There is limited research comparing the experience of multiple advanced music therapy trainings

Creating Persian-like music using computational intelligence

Dastgāh are modal systems in traditional Persian music. Each Dastgāh consists of a group of melodies called Gushé, classified in twelve groups about a century ago (Farhat, 1990). Prior to that time, musical pieces were transferred through oral tradition. The traditional music productions revolve around the existing Dastgāh, and Gushe pieces. In this thesis computational intelligence tools are employed in creating novel Dastgāh-like music.There are three types of creativity: combinational, exploratory, and transformational (Boden, 2000). In exploratory creativity, a conceptual space is navigated for discovering new forms. Sometimes the exploration results in transformational creativity. This is due to meaningful alterations happening on one or more of the governing dimensions of an item. In combinational creativity new links are established between items not previously connected. Boden stated that all these types of creativity can be implemented using artificial intelligence.Various tools, and techniques are employed, in the research reported in this thesis, for generating Dastgāh-like music. Evolutionary algorithms are responsible for navigating the space of sequences of musical motives. Aesthetical critics are employed for constraining the search space in exploratory (and hopefully transformational) type of creativity. Boltzmann machine models are applied for assimilating some of the mechanisms involved in combinational creativity. The creative processes involved are guided by aesthetical critics, some of which are derived from a traditional Persian music database.In this project, Cellular Automata (CA) are the main pattern generators employed to produce raw creative materials. Various methodologies are suggested for extracting features from CA progressions and mapping them to musical space, and input to audio synthesizers. The evaluation of the results of this thesis are assisted by publishing surveys which targeted both public and professional audiences. The generated audio samples are evaluated regarding their Dastgāh-likeness, and the level of creativity of the systems involved

Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn

An investigation of the benefits of improvisation for classical musicians

For centuries improvisation has been an integral part of European classical music culture. Until the nineteenth century most musicians were composers, improvisers and performers. Today, improvisation is less common in the classical music scene with most classical musicians being either performers or composers and only a minority of them having the ability to improvise. Is improvisation relevant to the classical musician whose main concern is the performance of written repertoire? Learning how to improvise and practicing improvisation requires a musician to develop particular skills which can be directly applied in the performance and interpretation of precomposed music. This make improvisation a valuable tool for the classical musician. The musician who improvises will have a deeper understanding of the music they are performing, an enhanced capacity to critically listen to the sound that they are producing and the ability to compose intuitively; becoming an inventor of the music as opposed to a reproducer of notes that they are reading or have memorised. Furthermore, the musician seeking to interpret repertoire from the seventeenth, eighteenth and nineteenth centuries should have an understanding of the thriving tradition of improvisation that existed throughout those eras and perform this repertoire with an awareness of that tradition. There are classical musicians today who perform improvisation and this is a practice that should be encouraged. However, even if a musician is not engaging with improvisation in this way, the process of learning to improvise and regular practice of it will assist musicians when interpreting repertoire and preparing for a performance. The final chapter of this paper provides an overview of how classical musicians might incorporate improvisation into their practice

A Category-Theoretic Compositional Framework of Perceptron-Based Neural Networks plus an Architecture for Modeling Sequences Conditioned to Time-Structured Context: An Implementation of a Generative Model of Jazz Solo Improvisations

This work introduces an algebraic graphical language of perceptrons, multilayer perceptrons, recurrent neural networks, and long short-term memory neural networks, via string diagrams of a suitable hypergraph category equipped with a concatenation diagram operation by means of a monoidal endofunctor. Using this language, we introduce a neural network architecture for modeling sequential data in which each sequence is subject to a specific context with a temporal structure, that is, each data point of a sequence is conditioned to a different past, present, and future context than the other points. As proof of concept, this architecture is implemented as a generative model of jazz solo improvisations