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We overview current problems of audio retrieval and time-series subsequence
matching. We discuss the usage of subsequence matching approaches in audio data
processing, especially in automatic speech recognition (ASR) area and we aim at
improving performance of the retrieval process. To overcome the problems known
from the time-series area like the...
Contexts in source publication
Context 1
... measures allows to compare sequence even if they have different lengths. It is allowed by the ability of time warping (See Fig. ...
Context 2
... sort of elasticity to the process of the time series comparison. It allows warping of sequences in a time to eliminate scaling or gaps on the time axis. Semantically, it means that for example in an audio retrieval process, we can identify a spoken word even if it was spoken in a different tempo than is the tempo of the indexed reference word. On Fig. 6 you can see the difference in a way of pairing values of the series being compared by fixed step Euclidean Distance and elastic DTW. Generally, DTW tries to find an optimal match between two sequences. More formally, Dynamic Time Warping is a linear programming method for finding a minimum cost path in an accumulation ...
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Citations
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Subsequence matching has appeared to be an ideal approach for solving many
problems related to the fields of data mining and similarity retrieval. It has
been shown that almost any data class (audio, image, biometrics, signals) is or
can be represented by some kind of time series or string of symbols, which can
be seen as an input for various subsequence matching approaches. The variety of
data types, specific tasks and their partial or full solutions is so wide that
the choice, implementation and parametrization of a suitable solution for a
given task might be complicated and time-consuming; a possibly fruitful
combination of fragments from different research areas may not be obvious nor
easy to realize. The leading authors of this field also mention the
implementation bias that makes difficult a proper comparison of competing
approaches. Therefore we present a new generic Subsequence Matching Framework
(SMF) that tries to overcome the aforementioned problems by a uniform frame
that simplifies and speeds up the design, development and evaluation of
subsequence matching related systems. We identify several relatively separate
subtasks solved differently over the literature and SMF enables to combine them
in straightforward manner achieving new quality and efficiency. This framework
can be used in many application domains and its components can be reused
effectively. Its strictly modular architecture and openness enables also
involvement of efficient solutions from different fields, for instance
efficient metric-based indexes. This is an extended version of a paper
published on DEXA 2012.
