Archive

# Features

- TBD

# Planned

- Stream segregation
- Rhythmic analysis
- Harmonic analysis
- Key inference

This is software for music theoretic programming in the Mathematica environment.

It contains several sub-packages that comprise symbolic representation of music scores and events, operations and transformations on musical objects, music data formats import/export, music notation and visualization, and generative composition.

The install process breaks down as follows:

1. Install a subversion client (optional, but recommended)
2. Obtain the project source code from the repository
3. Configure _Mathematica_ so that it will find the package
4. Install Lilypond (optional, but highly recommended)
5. Open and evaluate the demo notebook to verify that everything works

[http://lilypond.org/web/|LilyPond] is an open-source project that does music typesetting using a TeX-style syntax and similar layout-optimization algorithms. The MusicTheory\`Notation\` subpackage uses LilyPond as a rendering engine to present musical fragments directly in the _Mathematica_ notebook interface.

# Features

- Logical operators
- EnharmonicQ
- EnharmonicClassQ
- SameDurationQ
- Pitch operators
- PitchInstance + Interval
- Duration operations
- Duration + Duration
- Duration - Duration
- Dot[Duration] (Duration + 1/2 Duration)
- Duration * Integer|Rational
- Duration / Integer|Rational
- Duration^-1

# Planned

- Note operators

This dissertation addresses the problem of
musical knowledge acquisition by investigating humans' learning
of a novel and unfamiliar musical system. This chapter details
the novel musical system, including how and why it is used in
composition and in the following experiments.

The Bohlen-Pierce Scale

The human experience of music is a
complex process. The human brain recruits multiple neural
processes to convert signals from the outside world into the
multidimensional musical experience. These processes enable the
perception and memory of pitch, rhythm, melody, harmony,
tonality, and timbre, as well as knowledge of musical

Familiarity,
Expectation, and Preference

The present chapter
demonstrates the human ability to learn a new musical system.
Using the two finite-state musical grammars described in the
previous chapter, we explore the learning of new music via
passive exposure. In all experiments in the next two chapters,

The previous chapter demonstrated the
possibility that humans can learn grammatical and frequency
structures of sounds from limited exposure. Results suggest
that the human brain is efficient at learning relationships
between sounds and deriving a novel musical experience. Many
questions can be raised regarding this learning ability: what

The previous chapters have
shown conclusive behavioral evidence for the human ability to
learn the new musical system. In order to further characterize
this learning ability, this chapter moves beyond behavior and
into an investigation of neural mechanisms employed in the
learning of new music. By studying the temporal and spectral

Discussion

In a series of behavioral and
electrophysiological experiments, this dissertation has shown
that humans can rapidly learn a new musical system. The
knowledge acquired from exposure includes rote memory for
individual items, grammatical rules for large sets of items,
and sensitivity to the frequency structure underlying the

1. Partial Correlations

In all
behavioural experiments reported in this dissertation, probe
tone ratings were initially correlated with the overall
exposure frequencies of each pitch. This results in a
correlation strength r for each participant's ratings
before and after exposure. While the value of r was

I have experienced one of the most interesting

1. open AudioSculpt
2. open your file
3. do a Sonagram Analysis
4. do a Fundamental Frequency Analysis (only if the sound is harmonic)
5. do a Partial Tracking Analysis
6. File: Save Analysis As... -> Save Partial Tracking As...

Notes:

The [http://pyserial.sourceforge.net/|py-serial] module is required. This is not provided with the OS/X default python install. After download, to build an install, run "sudo python ./setup.py install" from the Terminal, within the source code directory.

# Catserial

These pages offer an overview of filters and their use in electroacoustic music.

There are many ways to debug code:

- In circuit debugger: Use the ICSD/ICSP port, a debugger, and the appropriate host software. Lets you step through, watch memory, set breakpoints. This probably does not work well when the USB module is enabled, since its operations are time-sensitive.

# Startup

On platforms with an on-board status LED, the LED will be lit or blinking when the device first initializes. The light will turn off when the user opens the device and begins communication. From this point onward the status lights are under user control.

# Pin Configuration

[inline-left:Figure1.jpg]

The foot/switch interface is provided by a soft but “grippy”
toroidal ring of molded rubber embedded in a hard PVC disk.
These disks are sold as “floor protectors” for furniture. A flat
ring of polyurethane foam with a peel-off adhesive is on the
opposite face of the disk. This foam provides the restoring force

[inline-left:Figure2a.jpg]
This pressure sensor use a printed circuit board (PCB)
containing dozens of adjacent conducting strips and a patch of
piezoresistive fabric made by EEonyx (http://eeonyx.com). The
measuring principle is basically that of the FSR except we have
exchanged the position of the conducting elements and
piezoresistor with respect to the foot.