Collaborative Artificial Intelligence in Music Production

The use of technology has revolutionized the process of music composition, recording, and production in the last 30 years. One fusion of technology and music that has been longstanding is the use of artificial intelligence in the process of music composition. However, much less attention has been given to the application of AI in the process of collaboratively composing and producing a piece of recorded music. The aim of this project is to explore such use of artificial intelligence in music production. The research presented here includes discussion of an auto ethnographic study of the interactions between songwriters, with the intention that these can be used to model the collaborative process and that a computational system could be trained using this information. The research indicated that there were repeated patterns that occurred in relation to the interactions of the participating songwriters

The Faculty Notebook, September 2016

The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost

SameSameButDifferent v.02 – Iceland

The history of computer music is to a great extent the history of algorithmic composition. Here generative approaches are seen as an artistic technique. However, the generation of algorithmic music is normally done in the studio, where the music is aesthetically valued by the composer. The public only gets to know one, or perhaps few, variations of the expressive scope of the algorithmic system itself. In this paper, we describe a generative music system of infinite compositions, where the system itself is aimed for distribution and to be used on personal computers. This system has a dual structure of a compositional score and a performer that performs the score in real-time every time a piece is played. We trace the contextual background of such systems and potential future applications

Harmony and Technology Enhanced Learning

New technologies offer rich opportunities to support education in harmony. In this chapter we consider theoretical perspectives and underlying principles behind technologies for learning and teaching harmony. Such perspectives help in matching existing and future technologies to educational purposes, and to inspire the creative re-appropriation of technologies

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

Evaluation of recommender systems in streaming environments

Evaluation of recommender systems is typically done with finite datasets. This means that conventional evaluation methodologies are only applicable in offline experiments, where data and models are stationary. However, in real world systems, user feedback is continuously generated, at unpredictable rates. Given this setting, one important issue is how to evaluate algorithms in such a streaming data environment. In this paper we propose a prequential evaluation protocol for recommender systems, suitable for streaming data environments, but also applicable in stationary settings. Using this protocol we are able to monitor the evolution of algorithms' accuracy over time. Furthermore, we are able to perform reliable comparative assessments of algorithms by computing significance tests over a sliding window. We argue that besides being suitable for streaming data, prequential evaluation allows the detection of phenomena that would otherwise remain unnoticed in the evaluation of both offline and online recommender systems.Comment: Workshop on 'Recommender Systems Evaluation: Dimensions and Design' (REDD 2014), held in conjunction with RecSys 2014. October 10, 2014, Silicon Valley, United State

The Faculty Notebook, September 2017

The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost