4 accepted papers
We present in this paper ByteCover, which is a new feature learning method for cover song identification (CSI). Byte-Cover is built based on the classical ResNet model, and two major improvements are designed to further enhance the capability of the model for CSI. In the first improvement, we introd…
Cover song identification is a challenging task in the field of Music Information Retrieval (MIR) due to complex musical variations between query tracks and cover versions. Previous works typically utilize hand-crafted features and alignment algorithms for the task. More recently, further breakthrou…
In recent years, several deep learning models have been proposed for cover song identification and they have been designed to learn fixed-length feature vectors for music tracks. However, the aspect of temporal progression of music, which is important for measuring the melody similarity between two…
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, th…