This paper presents a novel method to detect and distinguish ten frequently used audio effects in recordings of electric guitar and bass. It is based on spectral analysis of audio segments located in the sustain part of previously detected guitar tones. Overall, 541 spectral, cepstral and harmonic features are extracted from short time spectra of the audio segments. Support Vector Machines are used in combination with feature selection and transform techniques for automatic classification based on the extracted feature vectors. With correct classification rates up to 100% for the detection of single effects and 98% for the simultaneous distinction of ten different effects, the method has successfully proven its capability - performing on isolated sounds as well as on multitimbral, stereophonic musical recordings.

Authors: Stein, Michael; Abeßer, Jakob; Dittmar, Christian; Schuller, Gerald
Affiliations: Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany; Ilmenau University of Technology, Institute for Media Technology, Ilmenau, Germany(See document for exact affiliation information.)
AES Convention: 128 (May 2010) Paper Number: 8013
Publication Date: May 1, 2010 Import into BibTeX
Subject: Music Analysis and Processing
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=15310

Click to purchase paper as a non-member 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 $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 128Papers)   /128/8013.pdf

Start a discussion about this paper!