Sample-level sound synthesis with recurrent neural networks and conceptors

Conceptors are a recent development in the field of reservoir computing; they can be used to influence the dynamics of recurrent neural networks (RNNs), enabling generation of arbitrary patterns based on training data. Conceptors allow interpolation and extrapolation between patterns, and also provide a system of boolean logic for combining patterns together. Generation and manipulation of arbitrary patterns using conceptors has significant potential as a sound synthesis method for applications in computer music but has yet to be explored. Conceptors are untested with the generation of multi-timbre audio patterns, and little testing has been done on scalability to longer patterns required for audio. A novel method of sound synthesis based on conceptors is introduced. Conceptular Synthesis is based on granular synthesis; sets of conceptors are trained to recall varying patterns from a single RNN, then a runtime mechanism switches between them, generating short patterns which are recombined into a longer sound. The quality of sound resynthesis using this technique is experimentally evaluated. Conceptor models are shown to resynthesise audio with a comparable quality to a close equivalent technique using echo state networks with stored patterns and output feedback. Conceptor models are also shown to excel in their malleability and potential for creative sound manipulation, in comparison to echo state network models which tend to fail when the same manipulations are applied. Examples are given demonstrating creative sonic possibilities, by exploiting conceptor pattern morphing, boolean conceptor logic and manipulation of RNN dynamics. Limitations of conceptor models are revealed with regards to reproduction quality, and pragmatic limitations are also shown, where rises in computation and memory requirements preclude the use of these models for training with longer sound samples. The techniques presented here represent an initial exploration of the sound synthesis potential of conceptors, demonstrating possible creative applications in sound design; future possibilities and research questions are outlined

Inteligencia artificial aplicada en la música: una revisión sistemática de la literatura

Hoy en día, la inteligencia artificial es más común de lo que pensamos, cada vez son más las áreas donde se aplica la misma y la música no es la excepción. A raíz de este interés surgen investigaciones, las cuales proponen múltiples maneras de poder emplear los principios de la inteligencia artificial en la música. Por lo cual, la presente revisión sistemática de la literatura pretende reunir dichas investigaciones, en base a una serie de preguntas. El proceso de búsqueda se llevó a cabo en las bases de datos ProQuest Central e IEEE Xplore; la cantidad de trabajos se acotó a partir de filtros, los mismos que recuperaron los resultados automáticamente, además se hizo uso de criterios de inclusión, exclusión y calidad, los cuales demandaron un análisis para su aplicación y posterior selección de documentos. Luego de ello, se pudo contestar las preguntas de investigación planteadas. Las respuestas fueron muy interesantes, ya que, las aplicaciones de la inteligencia artificial en la música son diversas, llegándose a identificar un total de 11. Asimismo, el Machine learning presentó una mayor incidencia en las investigaciones. Finalmente, se llega a la conclusión que, a pesar, de que la música no es el área más popular donde se extrapola la inteligencia artificial, existen investigadores trabajando en proyectos que sirvan de aporte al mundo musical

Instrumental investigations at the Emute lab

This lab report discusses recent projects and activities of the Experimental Music Technologies Lab at the University of Sussex. The lab was founded in 2014 and has contributed to the development of the field of new musical technologies. The report introduces the lab ’s agenda, gives examples of its activities through common themes and gives short description of lab members’ work. The lab environment, funding income and future vision are also presented