Key-Invariant Convolutional Neural Network Toward Efficient Cover Song Identification

Cover song identification has long been a challenging task due to key, timbre and structure variations in different renditions of a song. Previous research mostly involves handcrafted features and sequence alignment methods, where further breakthroughs can hardly be achieved. In this paper, we utilize a supervised deep learning method to learn an effective feature extractor for cover song identification. Regarding it as a classification problem, we propose a key-invariant convolutional neural network robust against key transposition for classification. Having been trained, the network is used to extract representations of music, which could be used to measure the similarity between songs. Besides, the representations are highly sparse; effective algorithms can be devised to accelerate the computation. Experimental results show that our model achieves high precision with low time cost on several datasets. Especially, our method outperforms the state-of-the-art approach on one of the datasets.