Effect Recognition and Delay Estimation for a Guitar Effector
Date Issued
2010
Date
2010
Author(s)
Hsiao, Li-Wei
Abstract
Audio effects are commonly used in playing electric guitar. When playing others’ piece, guitar players often want to imitate the original guitar tone of the song and further create their own tone to tune process. However, because there are a lot of different effects, and there are many parameters on each effect, this tuning process is very tedious and needs to take much time for learning. In this thesis we let computer automatically recognize whether the input audio files which recorded guitar only have passed distortion effect or delay effect or neither (clean tone) based on SVM (Support Vector Machine). For delay effect, timbral features and time domain features of data for neural network and autocorrelation method can be applied in delay parameters estimation. The average accuracy of three delay parameters with autocorrelation method is 90.53%, and it’s significantly improved as opposed to neural network based method. The autocorrelation method can also detect whether the input data have passed delay effect, and the hit rate is 88.89%.
Subjects
effect recognition
delay parameters estimation
support vector machine
neural network
autocorrelation
Type
thesis
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