This blog has been eerily quiet of late, although that doesn’t mean I haven’t been up to anything. More things are being finished within the imminent future so here’s some teasers of things to come.
First up, I’ve been asked to present a keynote paper at this years Sound, Sight, Space and Play (SSSP), at De Montfort Uni, where I used to work many years ago. Here’s the abstract for the paper:
Real-time notation through Brain-Computer Music Interfacing
Brain waves have long been of interest to musicians as a viable means of input to control a musical system. Until recently research has focused on the voluntary control of alpha waves  , and event-related potentials time locked to stimuli, both of which fall short of explicit real-time control. This paper presents on-going research into utilising EEGtechniques from studies in neuroscience in the development of a Brain-Computer Music Interface (BCMI) as a precision controller in composition and performance.
Meaning in Brain Waves
Affordable and more portable hardware and faster signal processing has widened access for the development of bespoke BCMI tools, as well as presented fresh obstacles to overcome . Still, when working with electrical signals so minute, complex and highly prone to interference more work is needed to extract meaning within EEG. This paper identifies methods for alleviating these issues and approaches to mapping within BCMI systems.
This paper presents the BCMI performance piece Mind Trio, allowing a BCMI user to conduct a score, presented to a musician in real-time.
The automated composition process will take a set of pre-composed musical cells, which will continuously change slightly, by means of transpositions, change in tempo, replacement of notes, etc; guided by conductors explicit decisions.
1. Ortiz Perez MA, Knapp RB (2009) Biotools: Introducing a Hardware and Software Toolkit for fast implemetation of Biosignals for Musical Applications. Computer Music Modeling and Retrieval. Sense of Sounds: 4th CMMR Copenhagen, Denmark
2. Grierson M, Kiefer C Better Brain Interfacing for the Masses: Progress in Event-Related Potential Detection using Commercial Brain Computer Interfaces. 29th International Conference on Human Factors in Computing Systems, Vancouver, Canada, 2011.
3. Eaton J, Miranda E New Approaches in Brain-Computer Music
Interfacing: Mapping EEG for Real-Time Musical Control. In: Music, Mind, and Invention Workshop, New Jersey, USA, 2012.
A Stock Market Simulation for Open Outcry. Performance Model.
This is an quick overview of the current iteration of the stock market I’ve modeled for Open Outcry. Actually it’s more of a chance to document and summarise the reams of notes I’ve made into some short description of what the code actually does.
The market trades for a total of 10 years over 120 periods (months). It has three assets, each with a starting value, an annual expected return (how much the stock with be worth at the end of the 10 years), and an annual volatility (how much the price of the stock can vary over the 10 years).
The assets are statistically related to each other through a correlation coefficient (-1 to 1). A correlation of +1 implies that the two stocks will move in the same direction 100% of the time. A correlation of -1 implies the two stocks will move in the opposite direction 100% of the time. A correlation of zero implies that the relationship between the stocks is completely random.
In order to emulate a more realistic market certain climates that affect the value of the stock. periods of boom, stability or decline are tied in with news stories and can be triggered manually or built into a probability matrix as a generative process.
For example if the market has been in a boom period for a specific amount of time there could be a 20% chance of it staying in this regime, a 40% chance of it moving into decline and a 40 percent chance of it become stable, chosen at random. All of these parameters need to be defined and tested to create a specific type of market.
Each of these regimes have different parameters that affect the simulation of the market. For example in a boom period the expected annual returns will be larger than in other regimes, and in a stable market the correlation of assets might be more random due to there being little need for all assets moving together in one direction.
The video here shows the market moving in steps of one month. the market begins in a stable state where regimes are chosen based on probabilities. It then shows the results of a market being pushed into a boom period, then a bust period, then back to normal. For no particular reason I’ve added some recordings of a stream from different locations. Once the model is complete I might use the simulation as a compositional driver, just for fun.
This is a screen shot of the Emotiv EPOC using the open source software BrainBay. The graph on the left is displaying the FFT response of my brain waves, which are being stimulated by an interface I coded in pd GEM. This allows me to elicit control over music using a wireless and extremely portable device.
The Emotiv is a pretty noisy interface and unfortunately doesn’t provide the same precise response as Waverider and g-tec sensors, but this shows a system that can be built using much cheaper equipment and can be done away from the lab. The next step is to integrate this into my Max Score patches. Video to follow (some time, whenevs).
Here’s a good example of breaking the rules when it comes to mastering. This track has been recorded in an empty shop with a couple of mics. A main consideration when mastering this album was to preserve the natural dynamics of playing and of the space, but to keep it in the context of an album. So, as some tracks are naturally performed quieter than others, the relative volumes between the tracks needs to be preserved. As a result some tracks have a lot more headroom than others even after the mastering stage. This, as mastering should be, is based on the intended listening medium, which in this case is strictly for online streaming.
Processes used: EQ (subtractive to remove some boomy elements) - MB Compression (with very high thresholds) - Stereo Imaging (in the higher frequencies just to add a bit of breathing space) - Limiting (slow attack to preserve transients)