This study investigates the effect of reverberation on the accuracy of a musical instrument recognition model based on Line Spectral Frequencies and K-means clustering. 180 experiments were conducted by varying the type of music databases (isolated notes, solo performances), the stage in which the reverberation is added (learning and/or testing), and the reverberation type (3 different Reverberation Times, 10 different dry-wet levels). The performances of the model systematically decreased when reverberation was added at the testing stage (by up to 40%). Conversely, when reverberation was added at the training stage, a 3%-increase of performance was observed for the solo performances database. The results suggest that pre-processing the signals with a dereverberation algorithm before classification may be a means to improve musical instrument recognition systems.

Authors: Barthet, Mathieu; Sandler, Mark
Affiliation: Centre for Digital Music, Queen Mary University of London, London, UK
AES Convention: 128 (May 2010) Paper Number: 8110
Publication Date: May 1, 2010 Import into BibTeX
Subject: Audio Content Management—Audio Information Retrieval
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=15407

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