Embodying an Interactive AI for Dance Through Movement Ideation

What expectations exist in the minds of dancers when interacting with a generative machine learning model? During two workshop events, experienced dancers explore these expectations through improvisation and role-play, embodying an imagined AI-dancer. The dancers explored how intuited flow, shared images, and the concept of a human replica might work in their imagined AI-human interaction. Our findings challenge existing assumptions about what is desired from generative models of dance, such as expectations of realism, and how such systems should be evaluated. We further advocate that such models should celebrate non-human artefacts, focus on the potential for serendipitous moments of discovery, and that dance practitioners should be included in their development. Our concrete suggestions show how our findings can be adapted into the development of improved generative and interactive machine learning models for dancers’ creative practice

Predictive songwriting with concatenative accompaniment

Musicians often use tools such as loop-pedals and multitrack recorders to assist in improvisation and songwriting. While these devices are useful in creating new compositions from scratch, they do not contribute to the composition directly. In recent years, new musical instruments, interfaces and controllers using machine learning algorithms to create new sounds, generate accompaniment or construct novel compositions, have become available for both professional musicians and novices to enjoy. This thesis describes the design, implementation and evaluation of a system for predictive songwriting and improvisation using concatenative accompaniment which has been given the nickname PSCA. In its most simple form, the PSCA functions as an audio looper for vocal improvisation, but the system also utilises machine learning approaches to predict suitable harmonies to accompany the playback loop. Two machine learning algorithms were compared and implemented into the PSCA to facilitate harmony prediction: the hidden Markov model (HMM) and the Bidirectional Long Short-Term Memory (BLSTM). The HMM and BLSTM algorithms are trained on a dataset of lead sheets in order to learn the relationship between the notes in a melody and the chord which accompanies it as well as learning dependencies between chords to model chord progressions. In quantitative testing, the BLSTM model was found to be able to learn the harmony prediction task more effectively than the HMM model, this was also supported by a qualitative analysis of musicians using the PSCA system. The system proposed in this thesis provides a novel approach in which these two machine learning models are compared with regards to prediction accuracy on the dataset as well as the perceived musicality of each model when used for harmony prediction in the PSCA. This approach results in a system which can contribute to the improvisation and songwriting process by adding harmonies to the audio loop on-the-fly

PSCA sessions

<p>Sessions recorded using the Predictive Songwriting with Concatenative Accompaniment (PSCA) system,  as part of a masters thesis at the University of Oslo, 2018.</p

Using motion capture technologies to assess the degree of similarity for intangible cultural heritage expressions

After multiple cases of misappropriation of intangible cultural heritage expressions, indigenous communities must still rely on the public outcry to stop third parties from illegitimately exploiting their traditions. Beyond raising awareness of the pervasiveness of these practices, pragmatic tools have to be developed to strengthen the conservation and transmission of traditional cultural expressions in action, such as dances, rituals, and performances, with special attention to the digital environments wherein they circulate. To defy the apparent contradiction between technology and indigenous culture, the “Movement Similarity Project” explored new technologies of human-movement recognition to understand the possibilities of similarity algorithms and motion capture repositories to overcome the vulnerability in which indigenous dances dwell. These interdisciplinary efforts are raised to protect the interests and livelihoods of indigenous peoples in relation to their intangible cultural heritage, until new parameters to prevent their misappropriation arise on a global scale

The Tangibilization of Indigenous Dances and the Rehearsal of a Similarity Model for Quantitative Analysis of Movement

This article explores several tools of varied affordability within the field of computer-based technologies of human movement recognition as a means of responding to the current lack of protection extended to Indigenous dances. Following a general theoretical overview of new technologies developed to process human movement, including motion capture, video visualization, and computer vision, this paper offers an investigation into the practical applications of such technology when applied to dance. The Movement Similarity Project at the University of Oslo’s RITMO Centre is explored as a case study, in which motion-capture technology has been utilized to measure and quantify the degree of similarity between different dance recordings. The possibilities, limitations, and future directions of these technologies are evaluated according to their ability to safeguard Indigenous dances.Este artículo explora varias herramientas de variada asequibilidad dentro del campo de las tecnologías informáticas de reconocimiento del movimiento humano como un medio para responder a la actual falta de protección de las danzas indígenas. Tras una descripción teórica general de las nuevas tecnologías desarrolladas para procesar el movimiento humano, incluida la captura de movimiento, la visualización de video y la visión por computadora, este artículo ofrece un recuento de sus aplicaciones prácticas para el campo de la danza. Esta es la experiencia del “Proyecto de Similitud del Movimiento” realizado en el RITMO Centre de la Universidad de Oslo, aquí analizado como estudio de caso, en el que la tecnología de captura de movimiento se utilizó para medir cuantitativamente el grado de similitud entre dos danzas. Las posibilidades, limitaciones y direcciones futuras de estas tecnologías son evaluadas de acuerdo con su capacidad para salvaguardar las danzas indígenas

Reinforcement Learning Based Dance Movement Generation

Generating genuinely creative and novel artifacts with machine learning is still a challenge in the world of computational science. A creative machine learning agent can be beneficial for applications where novel solutions are desired and may also optimize search. Reinforcement Learnings’ (RL) interactive properties can make it an effective tool to investigate these possibilities in creative contexts. This paper shows how a Reinforcement learning-based technique, in combination with Principal Component Analysis (PCA), can be utilized for generating varying movements based on a goal picking policy. The proposed model is trained on a data set of motion capture recordings of dance improvisation. Our study shows that the trained RL agent can learn to pick sequences of dance poses that are coherent, have compound movement, and can resemble dance