14,263 research outputs found
Linear Algorithm for Conservative Degenerate Pattern Matching
A degenerate symbol x* over an alphabet A is a non-empty subset of A, and a
sequence of such symbols is a degenerate string. A degenerate string is said to
be conservative if its number of non-solid symbols is upper-bounded by a fixed
positive constant k. We consider here the matching problem of conservative
degenerate strings and present the first linear-time algorithm that can find,
for given degenerate strings P* and T* of total length n containing k non-solid
symbols in total, the occurrences of P* in T* in O(nk) time
Searching digital music libraries
There has been a recent explosion of interest in digital music libraries. In particular, interactive melody retrieval is a striking example of a search paradigm that differs radically from the standard full-text search. Many different techniques have been proposed for melody matching, but the area lacks standard databases that allow them to be compared on common grounds––and copyright issues have stymied attempts to develop such a corpus. This paper focuses on methods for evaluating different symbolic music matching strategies, and describes a series of experiments that compare and contrast results obtained using three dominant paradigms. Combining two of these paradigms yields a hybrid approach which is shown to have the best overall combination of efficiency and effectiveness
IDENTIFICATION OF COVER SONGS USING INFORMATION THEORETIC MEASURES OF SIMILARITY
13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted version13 pages, 5 figures, 4 tables. v3: Accepted versio
Query-based Deep Improvisation
In this paper we explore techniques for generating new music using a
Variational Autoencoder (VAE) neural network that was trained on a corpus of
specific style. Instead of randomly sampling the latent states of the network
to produce free improvisation, we generate new music by querying the network
with musical input in a style different from the training corpus. This allows
us to produce new musical output with longer-term structure that blends aspects
of the query to the style of the network. In order to control the level of this
blending we add a noisy channel between the VAE encoder and decoder using
bit-allocation algorithm from communication rate-distortion theory. Our
experiments provide new insight into relations between the representational and
structural information of latent states and the query signal, suggesting their
possible use for composition purposes
Normalized Information Distance
The normalized information distance is a universal distance measure for
objects of all kinds. It is based on Kolmogorov complexity and thus
uncomputable, but there are ways to utilize it. First, compression algorithms
can be used to approximate the Kolmogorov complexity if the objects have a
string representation. Second, for names and abstract concepts, page count
statistics from the World Wide Web can be used. These practical realizations of
the normalized information distance can then be applied to machine learning
tasks, expecially clustering, to perform feature-free and parameter-free data
mining. This chapter discusses the theoretical foundations of the normalized
information distance and both practical realizations. It presents numerous
examples of successful real-world applications based on these distance
measures, ranging from bioinformatics to music clustering to machine
translation.Comment: 33 pages, 12 figures, pdf, in: Normalized information distance, in:
Information Theory and Statistical Learning, Eds. M. Dehmer, F.
Emmert-Streib, Springer-Verlag, New-York, To appea
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