The project I've been working on for the last eight months has now released our first public release. Humtap is a mobile app for collaborative music making. Users collaborate by each recording hums and taps into their mobile phones. These audio recordings are transcribed and become musical avatars. A user can then create a piece of music (currently of the electronica genre) by combining their hum or tap with another user's tap or hum, or with their own tap or hum. Finally, users can also create a new piece of music by combining two other user's hum and tap. The app combines the transcriptions in a musical way and selects the electronic instrumentation.
Reprising the show we did a couple of years back, a "leaner" Pleasure Programmes, consisting of Elizabeth Soychak and myself, play a couple of tunes at the Theatre for the New City as part of the Lower East Side Festival on May 23rd. We did a couple of tunes in a packed programme of different performers, this is our version of See See Rider by Ma Rainey, made famous by Ella Fitzgerald.
Reprising the show we did a couple of years back, a "leaner" Pleasure Programmes, consisting of Elizabeth Soychak and myself, play a couple of tunes at the Theatre for the New City as part of the Lower East Side Festival on May 23rd. We did a couple of tunes in a packed programme of different performers, this is our version of Corcovado by Antonio Carlos Jobim.
Since March, I am now independently contracting to a number of companies doing music and audio DSP and machine learning. This is a great opportunity for me to work with and advise a number of really great companies, particularly small startups, on commercial applications of audio signal processing, machine learning, information retrieval, cloud infrastructure. You can check my biography for more details.
I have the joy of owning an Ibanez IMG-2010 Guitar Synth, which can be had quite cheap on Ebay, yet are excellent quality Steinberger style guitars, and originally sold for quite a princely sum. Wayne Joness' very informative GR-300 site extolls the qualities of this beast in great detail. I'm yet to do the conversion to a DB-25 pin connector and fit the G-202 hex fuzz circuit, but it's planned.
I've had an Apple Airport Express 1st Generation, 802.11g model A1084 since new, c. 2004. This has long been superseded by newer versions, and for sometime it was just doing duty for me as a USB print server, not as a router. However, it seems that there is a bug introduced around v6.2 of the firmware that would cause it to go offline when configured to "join wireless network". Restarting the AE would allow it to run, but it would soon drop off the net. It became particularly troubling as it would become unresponsive almost as soon as it was configured, barely even allowing a single print job to be sent. It's not clear what the cause is or where exactly the bug may lie.
I'm woefully late in pointing this out, but there is now a video done by Matt Hines and Jay Leboeuf explaining MediaMined.
Probing neural mechanisms of music perception, cognition, and performance using multivariate decoding
Psychomusicology: Music, Mind and Brain, 22(2):168–174, 2012
Recent neuroscience research has shown increasing use of multivariate decoding methods and machine learning. These methods, by uncovering the source and nature of informative variance in large data sets, invert the classical direction of inference that attempts to explain brain activity from mental state variables or stimulus features. However, these techniques are not yet commonly used among music researchers. In this position article, we introduce some key features of machine learning methods and review their use in the field of cognitive and behavioral neuroscience of music. We argue for the great potential of these methods in decoding multiple data types, specifically audio waveforms, electroen- cephalography, functional MRI, and motion capture data. By finding the most informative aspects of stimulus and performance data, hypotheses can be generated pertaining to how the brain processes incoming musical information and generates behavioral output, respectively. Importantly, these methods are also applicable to different neural and physiological data types such as magnetoencephalography, near-infrared spectroscopy, positron emission tomography, and electromyography.