Blind bandwidth extension using K-means and Support Vector Regression

Chih-Wei Wu, Mark Vinton

Abstract: In this paper, a blind bandwidth extension algorithm for music signals has been proposed. This method applies the K-means algorithm to firstly cluster audio data in the feature space, and constructs multiple envelope predictors for each cluster accordingly using Support Vector Regression (SVR). A set of well-established audio features for Music Information Retrieval (MIR) has been used to characterize the audio content. The resulting system is applied to a variety of music signals without any side information provided. The subjective listening test results show that this method can improve the perceptual quality successfully, but the minor artifacts still leave room for future improvements.