Thoughts about Memex

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Thoughts about Memex

Shlomo Dubnov


ese notes describe

the ideas and the algorithms that were used for
creation of a computer composition for violin “Memex”. The work
consist of a
recombination of phrases by Bach, Mozart

and Beethoven

using i
deas from universal coding and machine learning,
the notes. It also represents

approach to music modeling as an
information source,
which opens new

possibilities for style learning,
mixing and
experimenting with various music
listener re
lations based
memories, expectations and surprises.

emex is a computer artifact, a composition resulting from mathematical operations on a
database of musical works that was designed and created by
the author
. The name of the piece
comes from an ar
ticle by Vannevar Bush

, 19
, where he described a futuristic device “in
which an individual stores all his books, records, and communications, and which is mechanized
so that it may be consulted with exceeding speed and flexibility... When the user is building a
trail, he

names it, inserts the name in his code book, and taps it out on his keyboard. Before him
are the two items to be joined, projected onto adjacent viewing positions. At the bottom of each
there are a number of blank code spaces, and a pointer is set to indi
cate one of these on each
item. The user taps a single key, and the items are permanently joined".

The idea of building memory trails, joining of information and deriving new meanings is
designed into the composition Memex in a very formal and algorithmi
cally precise manner.
What we hear is new music, where every note belongs to one of the great masters, either Bach,
or Mozart or Beethoven. Works by these composers were analyzed using information processing
algorithms to be described below, creating an au
tomaton that can travel across the web of
musical associations, leaving a trail of memories, expectations and surprises. The work is
provocative, intended to leave the listener perplexed conceptually, aesthetically, and may be

Experiments wit
h music models using IT methods (usually named musical style learning) are
now almost
decade old. In many respects, these works build upon a long musical tradition of
l modeling that began with Hill
er and
"Illiac Suite"


in the 50th and the
French composer / mathematician / architect Xenakis

using Markov chains and stochastic
processes. My experiments with machine learning of musical style began with a simple mutual
source algorithm
suggested by El
Yaniv et al.


that was made to jump between different
musical sources looking for the longest matching suffix, effectively creating a new source that is
closest in terms of cross
entropy to the original musi
cal sequences. The next step in experiments,
done wit
h Assayag et al.


was learning of musical works using compression algorithms,
specifically the Lem

incremental parsing (IP) algo
rithm for creation of context
dictionary and probability assignment as suggested by Feder and Merhav's

prediction. Performing a random walk on the phrase dictionary with appropriate probabilities for
s generated new music.

These works achieved surprisingly credible musical results in terms of style imitation. Some
informal testing suggested that people could not distinguish between real and computer
improvisation for about 30
40 s
econds. This was im
portant for
showing that major aspects of
music can be captured without explicit coding of musical rules or knowledge. Additional
experiments were done usi
ng Ron et al.

robabilistic Suffix Tree (P

machine learning
method, trying to improve on "generalization" capabilities of the statistical models at the cost of
some extra "false notes" resulting from "lossy compression".

Memex presents a new approach

using Allauzen et al.

Factor Oracle (FO)

for generation
of new music from examples. FO is an automaton that is functionally equivalent to a suffix tree,
but with much fewer nodes. In comparison to IP and
PST trees that discard substrings, FO is
preferred because it can be built quickly and like the suffix tree it encodes all possible substrings.
One of the main properties of FO is that it indexes the sequence in such a way that at every point
along the dat
a it builds a pointer to future continuations for most recent suffixes that appeared in
that place. By "recent suffixes" we mean suffixes that occur for the first time when a new symbol
is observed. Since FO is constructed in online manner, all "previously

seen" suffixes are detected
earlier in the sequence. So, at every point along the sequence FO provides pointers to
continuations of most recent suffixes, and a pointer back to the longest repeating suffix. This
way, we can either jump into the “future” b
ased on the most recent past, or go to earlier past to
look for continuations of previously encountered suffixes (i.e. suffixes of shorter prefixes), and
so on. So, instead of considering best context with log
loss "gambling" on the next note, the new
hod operates by "forgetting" and selective choice of historical precedence for deciding about
the future.

The piece Memex for violin is created by such "random walk" over an FO that was constructed
from a collection of works by Bach, Mozart and Beethove
n. Prior to construction of FO, the
music material was analyzed in short times to construct a set of events (individual or
simultaneous notes and chords become symbols in a new sequence). This is needed to represent
polyphony (account for simultaneous note
s) and deal with invariance and possible symmetries.
At generation step the algorithm randomly chooses (in this piece with probability .87) to
continue to next state (advance along the original sequence) or jump back (with probability .13)
along the suffix

link and follow from there to any forward link. As explained above, this
procedure effectively uses the longest repeating suffix of the sequence to perform transitions to a
new place where continuation of this suffix can be found.

Music, in its pure for
m, is devoid of symbolism, denotation or concrete meanings, which makes
it a powerful “probe” into higher functions of our mind.
In terms of information theoretic

modeling this research goes beyond modeling and recreation of the source entropy. Considering


listener relations as an information channel opens new

to definition of musical
anticipations, memory and its relations to human cognitive responses. In this sense, Memex can
be used as a tool for investigating new insights into musical theor
y an

musical perception,
raising some interesting thoughts about what composing and listening actually means: What is
the style of the piece? What is its form, story, its meaning? If “controlling” the automaton
amounts to varying anticipations and memorie
s, does this lead to new insights about play of
cognition, creativity, or new venues for art making? How is listener experience related to
pervious training on related musical examples? Where does the free will of the composer / artist /
creator end and se
reproduction of culture begins?

Richard Moore, a computer music professor in UCSD, wrote about the piece:

"Have you ever had a lucid dream? While not exactly common, lucid dreams are ones in which
the dreamer somehow becomes aware that the experience
progress is a dream. Once you
know you’re dreaming (I have occasionally had this experience), you can relax. Sometimes,
lucid dreamers just wake up. However, they can sometimes elect to continue the dream,
exercising various levels of influence over wha
t is going on. One can elect to fly, to fulfill sexual
fantasies, to explore death, or life in other dimensions. Fantasy becomes the ruler of experience.
Exactly what many people want out of life.

If one were to elect to hear music in a lucid dream, what w
ould it sound like? Clearly, any such
music would not be constrained by rules, such as those of radio stations, music theory, gravity, or
social convention. Whatever such fantastic music might be based on, it is hard to imagine any
sense in which it would
not be based on memory. If necessity is the mother in invention, then
memory is its father, for how could anything appear in the mind that is not the product of
(possibly rearranged) memory?

Besides memory, there is an additional source of creativity, desc
ribed by many people, perhaps
most famously by Leonardo da Vinci. He is reputed to have used a technique of staring at stains
on walls, or patterns in mud, or splatters of paint, to see what they might suggest. Any child who
has found rabbits or ships in t
he sky while staring at clouds has done the same thing. Japanese
artists suggest tigers and rivers and billowing drapes with but a few brushstrokes. The human
mind has a powerful penchant for inference. Mostly, this capacity is used to make “sense” of the
sensorial world: we see, hear, feel, taste, or smell, and almost immediately
. Once we’ve
inferred the rabbit in the cloud, it becomes difficult

to see it there, even when we remember
that’s it’s “just a cloud.” Such inference is very fast, fa
ster than the speed of thought, especially
logical thought. It is not hard to imagine how those of our predecessors who quickly inferred the
toothed tiger behind the bush from a few flashes of light would have more likely survived
to become our ances

No one yet knows what sleep is, nor why we do it, nor why we dream, but I have a theory about
the last, which others have corroborated. Whatever else happens during sleep, the body shuts
down in certain ways. In particular, sensory input seems to be
greatly attenuated, though not
entirely shut off (thus, we can still be awakened by a sudden crash of thunder). The brain, freed
from most sensory input, doggedly continues to interpret what is going on. That which is
interpreted is somewhat unclear, but i
t seems that it is chaotic (that is, greatly affected in
unpredictable ways by tiny changes in both external and internal stimuli). The information that
comes into the brain during sleep seems both random and complex, which allows it to be
characterized st
ochastically, as with the heat
dance of molecules in a warm fluid (from which
Einstein established the existence of atoms). Even random information is subject to the brain’s
“interpreter,” which apparently never sleeps. The result is dreams, which (accordi
ng to my
theory) are the brain’s interpretations of chaotically appearing snippets of memories combined
with nearly nonexistent, random sensory inputs. Technically, a random signal is noise. Thus, the
food of dreams is memory spiced with noise.

Could we ex
plore the world of music that might be intentfully invoked in lucid dreams? One way
would be to enhance our ability to dream lucidly. Some people “practice” lucid dreaming by
various methods, and report varying degrees of success. Others, apparently, never

dream lucidly.
Your mileage may vary.

A computer scientist might use another method. Compared with brains, computers are fairly
primitive devices. Even to the limited extents that we understand them, the memory and
processing capacities of computers and b
rains still differ by many orders of magnitude (though
some researchers have pointed out that computers are growing in capacity at a rate much greater
than human brains). The most capable current supercomputers have capacities measured in
impressive units
like teraflops and petabytes. Might it be possible to explore musical memory in
a way similar to lucid dreaming on a computer by assembling fleeting “snippets” taken from one
or more sections of the vast domain of musical literature according to stochastic

(i.e., random)

The answer is yes. Without going into technical details, this is the essence of a method used in,
what? assembling? composing? extricating? snippetizing? dreaming? music for violin solo by
Shlomo Dubnov, a music professor with a b
ackground in computer science at UCSD. Dubnov’s
recent composition
, performed recently by UCSD violinist

, is based on
recollections of detailed musical moments taken from the violin literature of Bach, Mozart and
Beethoven (and presumab

by extension

anyone). The music retains a familiar quality, even
though it is obviously previously unheard. It is not like music composed by a student attempting
to imitate the style of one of these composers. It is the original music, presented in a w
completely unheard
yet. It would never be mistaken for Bach, or Mozart, or Beethoven, yet,
every note was, in some ultimate sense, was written by these composers.

A related technique has been used by another music professor with a background in compu
science: David Cope at UCSC has produced a CD entitled
Bach by Design
, in which Bach’s
music is used as a database for “deriving” additional music “by” Bach (even though Bach never
wrote it). Cope also based other “derived” music on the works of other
composers, with varying
degrees of verisimilitude. His stated motivation for such work is the desire to hear more music
from composers of the past that he has known and loved

more, even than they wrote!

Cope is clearly attempting to capture the essence of the musical style of various composers of the
past, while Dubnov is attempting something different. Dubnov’s “lucid music” touches on
something essential about the musical nature of mind, of intelligence
, of consciousness itself. It
is not about producing more violin pieces by past composers. It is a musical tool for the
exploration of mind, and its boundless ability to fail to interpret.”


The p
ce is written and dedicated to

, whose enthusiasm of experimental art is
never ceasing and whose intellectual curiosity inspired this work




"As We May Think", in the Atlantic Monthly, July 1945.


Hiller, L

A. and L

M. Isaacson. “
Experimental Music: Composition
With An Electronic
New York: McGraw Hill, 1959



Formalized Music: Thought and Mathematics in Composition

University Press, 1971





Fine and N

Tishby, "Agnostic Classification of Markovian Sequences",
in A
dvances in Neural Information

Processing Systems, Vol. 10, 1998


Dubnov, S., G. Assayag, O. Lartillot, and G. Bejerano, "Using Machine
Methods for Musical Style Modeling", IEEE Computers, 36 (10), pp. 73
80, Oct. 2003.


and A. Lempel, “Com
pression of Individual Sequences via Variable Rate
Coding,” IEEE Trans. Information Theory, vol. 24, no. 5, 1978, pp. 530




N. Merhav, and M. Gutman, “Universal Prediction of Individual Sequences,”
IEEE Trans.

Information Theory, vol. 38, 19
92, pp. 1258


Ron, D., Y. Singer, and N. Tishby, “The Power of Amnesia: Learning Probabilistic
Automata with Variable

Memory Length,” Machine Learning, vol. 25, 1996, pp. 117


Assayag, G. and S. Dubnov, “Using Factor Oracles for Machine Improvis
ation”, Soft
Computing 8, pp. 1432
7643, September 2004


Allauzen C, Crochemore M, Raffinot M,

Factor oracle: a new structure for pattern

, in Proceedings

of SOFSEM’99, Theory and Practice of Informatics, J.
Pavelka, G. Tel and M. Bartosek ed., M

Czech Republic, Lecture Notes in
Computer Science pp. 291

306, Springer
Verlag, Berlin