Filter feature selection methods are utilized to select discriminative terms from high-
dimensional text data to improve text classification performance and reduce computational …

Feature extraction is a crucial part of many MIR tasks. In this work, we present a system that
can automatically extract relevant features from audio for a given task. The feature extraction …

The GTZAN dataset appears in at least 100 published works, and is the most-used public
dataset for evaluation in machine listening research for music genre recognition (MGR). Our …

In this work, we present the Klout Score, an influence scoring system that assigns scores to
750 million users across 9 different social networks on a daily basis. We propose a …

Much work is focused upon music genre recognition (MGR) from audio recordings, symbolic
data, and other modalities. While reviews have been written of some of this work before, no …

The trend for listening to music online has greatly increased over the past decade due to the
number of online musical tracks. The large music databases of music libraries that are …

Text documents usually contain high dimensional non-discriminative (irrelevant and noisy)
terms which lead to steep computational costs and poor learning performance of text …

Genres are one of the key features that categorize music based on specific series of
patterns. However, the Arabic music content on the web is poorly defined into its genres …

Computational scientists have seen a frustrating trend of stagnating application performance
despite dramatic increases in the claimed peak capability of high performance computing …

The feature selection method plays a crucial role in text classification to minimizing the
dimensionality of the features and accelerating the learning process of the classifier. Text …