OnE with viri – lopezdonado + barret


Part of the last trogotronic comp – proud to be part a w t nelson endeavour. available from:


QLD Conservatorium Electroacoustic Ensemble


Live electroacoustic improv. Jesus LopezDoNaDo, Lloyd Barrett, Mitch, Sedelle, Sophia, Tim and Holly

As part of my doctorate in musical arts at the Quensland Conservatorium, I recently started contributing to the Electroacoustic Ensemble (EA). The EA is moderated by composer Lloyd Barrett who is being lately producing very interesting music with a tool called metasynth: [check his music]. So far it’s being a pleasure to work with the ensemble and it is of great value for my composition-practice since the EA is a safe place to experiment with unstable/new improvisation live-sets and compositional ideas.

A set using a cocoquantus processing a feedback loop from a grand piano being concominatly exited by a sustainiac guitar sustainer pedal.

The aim of the EA at the QLD-Con is: ” This ensemble provides an opportunity to work with computers, electronic devices and acoustic instruments in the creation of live performance works that do not rely on traditional notation or the pitch/duration paradigm.  Students will collaborate to develop soundscapes from flexible performative frameworks that emphasise audio-visuality, dynamic timbre, spatial/environmental awareness and human movement/interaction “

Holly was building an Auduino contraption.

Another EA member, Sedelle, heavily processes her trumpet with effects and looper pedals.

Music from Mental States: Data Base Generation

A diagram on preliminary ideas for data generation during initial stages of my PhD on Music Composition at the QLD Con. The proccess start with a series of precomposed themes that will be feed into tyhe system as audio files. Individual will listen to each them and during this process several sources of data will be explored – i.e.

(i) Mental State Examination and Diferential Diagnosis;

(ii) Selected physiological parameters traditional known to correlate with mental state changes, i.e. skin impedance, pulse rate, respiratory rate, blood pressure and body temperature. During this process of data acquisition some individual demographics will be recorded too, i.e. age, sex, gender, etc.

(iii) Electroencephalographic activity – these will be collected with the minimality invasive and readily available Emotiv EPOC EEG system.

Then i-iii, along with the predetermined ‘audio themes’ will be subject to a, still to be envisaged, process of dimensionality reduction and parametrization which will populate a database.

This database will be subjected to Data Mining proccess in order to extract Mental State Biased Compositional Rules. For instance, decision trees, cluster representaions, or just knowledege from sensitivity analysis.

Pls – feel free to comment by posting in this space.


Doctorate in Composition: Proposal

Discovering mental-state biased compositional paradigms for microtonal electro acoustic instruments

The idea is to explore the mathematics /mental-state/music-perception continuum by using artificial intelligence (AI) techniques applied to music composition. This shall not be a scientific endeavor but rather the generation of a compositional framework that will guide my compositional outcomes during the last stages of my research time at the Conservatorium. Not only musical pieces will be composed, but also, specific electro-acoustic instruments will be designed for these. Therefore, this proposal pretends to be a project on electro-acoustic music composition. Finally, despite this proposal is based on a rigorous and highly systematic approach, the final outcomes will be exceedingly modulated by compositional creativity, previous compositional experience, my cultural background and ultimately my vision of the world. Artificial Intelligence for the generation of a compositional framework AI or machine learning approaches can be either supervised or unsupervised. As I shall potentially apply them to composition, supervised methodologies attempt to generate compositional paradigms by learning from previous experiences sampled from human or previous compositions! input. Unsupervised techniques use well-known mathematics/biology inspired algorithms, which are universal, or al lest live within their own realm, and attempt to loosely classify a compositional space of themes.

Whether we considering supervised or unsupervised approaches, the target – or motivation – would be the listener mental state as induced by short musical passages (or themes). All themes would have to be as de-correlated as possible to western traditional musical forms to rule out trivial compositional/cultural biases, i.e. minor vs major triads, simple vs odd signatures, or traditional jazz forms. Ultimately, a large number of microtonal systems will be considered.

Themes will also have to be parameterized in terms of their dynamics, timbers, tempo, and other second order mathematical measures of music, i.e. density of microtonal system, melodic range, counterpoint descriptives, and second order measures like the Laplacian on frequency time series, etc. Also, a rigorous notation system should be adopted to facilitate discussing themes from an aesthetic perspective and to allow interpreters to generate the themes in a way that is minimizes ambiguity. Nonetheless, such notation system= should be open-ended to allow for moderninterpretation but also with little dichotomies so it could be easily translated into natural language to be interpreted by the AI algorithms. I would preliminary use an adaptation of Schenker diagrams and perhaps lilypond package as a computational framework.

Given, such archive of the themes (many issues to solve here – see challenges below), listeners will contribute their mental state by choosing from a set of images while listening to the music. This will annotate set of themes, hopefully, with, at least, pseudo-universal states of mind. If universality of theme and mental-states pairs is not achieved, this should not pose any constrain since the objective of this project is to generate a “guiding” compositional paradigm rather than proving that persons are universally biased in the same way by the same musical forms.

After the themes are classified and an annotated archive is generated, a neural network (a supervised AI method) will be used to learn compositional paradigms that are meant to induce mental states – again hopefully with certain level of universality. These neural networks could also be potentially used to augment the themes-set by generating new themes in a generative basis. Concomitantly, Knohonen maps (an unsupervised AI approach) will be generated to discover clusters of compositional parameters – clues – that might also correlate with mental sates and subsequently enrich my compositional framework.

If all goes well [nervousness pause] we should end up with an intelligent artificial framework that will allow composers to guide their creative process based on (1) themes annotate by metal states they induce; (2) Neural networks that will generate themes that will potentially generate predetermined mental sates; (3) Kohonen clusters of compositional and improvisational clues that should either induce mental state sequences in the listener or perhaps augment/inform the performance experience of a given his/her current mental-state.

With this intelligent framework at certain level of maturity, I intend to achieve the following objectives: (1) Generate a series of compositions based on predetermined mental state sequences. Ideally, the compositions will include a great deal of improvisation guidelines also based on the discovered compositional paradigms that shall be adopted by the interpreter/improviser at he/she leisure as informed by they current mental state. (2) Since the paradigms departed from highly open-ended themes (i.e., micro tonality, and variations of the real-time variations electronics) instruments will be specially designed and constructed for these pieces. These instruments will be based on traditional musical instruments with altered tunings, spatial arrangements, and electro-acoustic modifications.

Kraig Grady’s Lake Aloe: microtonal vibraphone