Professor Korea University Seongbuk-gu, Seoul-t'ukpyolsi, Republic of Korea
Abstract: A novel dictionary learning approach that utilizes Mel-scale frequency warping in detecting overlapped acoustic events is proposed. In this paper, we propose a method of frequency warping for better sound representation, and apply dictionary learning by a holistic-based representation, namely nonnegative K-SVD (NK-SVD) in order to resolve a basis sharing problem raised by part-based representations of NMF. By using Mel-scale frequency warping, we show that the information carried by low frequency components more than high frequency components and dealt with a low complexity. Also, the proposed holistic-based representation method avoids the permutation problem between another acoustic events. Based on these benefits, we confirm that the proposed method of Mel-scale with NK-SVD delivered significantly better results than the conventional methods.