Synchronized Swept-Sine: Theory, Application, and Implementation - October 2015
Effect of Microphone Number and Positioning on the Average of Frequency Responses in Cinema Calibration - October 2015
The Measurement and Calibration of Sound Reproducing Systems - July 2015
Feature Selection vs. Feature Space Transformation in Music Genre Classification Framework
Automatic classification of music genres is an inherent field of music information retrieval research. Nearly all state-of-the-art music genre recognition systems start from the feature extraction block. The extracted acoustical features often could be correlated or/and redundant, which can course various difficulties on the classification stage. In this paper we present a comparative analysis on applying supervised Feature Selection and Feature Space Transformation algorithms to reduce the feature dimensionality. We discuss pro and contra of the methods and weigh the benefits of each one against the others.
Click to purchase paper or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $20 for non-members, $5 for AES members and is free for E-Library subscribers.