Effective Cover Song Identification Based on Skipping Bigrams

Abstract

So far, few cover song identification systems that utilize index techniques achieve great success. In this paper, we propose a novel approach based on skipping bigrams that could be used for effective index. By applying Vector Quantization, our algorithm encodes signals into code sequences. Then, the bigram histograms of code sequences are used to represent the original recordings and measure their similarities. Through Vector Quantization and skipping bigrams, our model shows great robustness against speed and structure variations in cover songs. Experimental results demonstrate that our model achieves better performance than recent methods and is less computationally demanding.