Musical Signal Type Discrimination based on Large Open Feature Sets

Automatic discrimination of musical signal types as speech, singing, music, genres or drumbeats within audio streams is of great importance e.g. for radio broadcast stream segmentation. Yet, feature sets are largely discussed. We therefore suggest a large open feature set approach starting with systematical generation of 7k hi-level features based on MPEG-7 Low-Level-Descriptors and further feature contours. A subsequent fast Gain Ratio reduction followed by wrapper-based Floating Search leads to a strong basis of relevant features. Next, features are added by alteration and combination within genetic search. For classification we use Support-Vector-Machines proven reliable for this task. Test-runs are carried out on two task-specific databases and the public Columbia SMD database and show significant improvements for each step of the suggested novel concept.

• Type: Conference
• Year: 2006
• Where: ICMCS
• Authors: Björn Schuller, Frank Wallhoff, Dejan Arsic, Gerhard Rigoll
• Keywords: Broadcast Stream Segmentation, Feature Set, Feature Set Approach, ICMCS 2006