Da-TACOS: A dataset for cover song identification and understanding

Open Access

Abstract

Comunicació presentada a: 20th annual conference of the International Society for Music Information Retrieval (ISMIR) celebrat del 4 al 8 de novembre de 2019 a Delft, Països Baixos. ; This paper focuses on Cover Song Identification (CSI), an important research challenge in content-based Music Information Retrieval (MIR). Although the task itself is interesting and challenging for both academia and industry scenarios, there are a number of limitations for the advancement of current approaches. We specifically address two of them in the present study. First, the number of publicly available datasets for this task is limited, and there is no publicly available benchmark set that is widely used among researchers for comparative algorithm evaluation. Second, most of the algorithms are not publicly shared and reproducible, limiting the comparison of approaches. To overcome these limitations we propose Da-TACOS, a DaTAset for COver Song Identification and Understanding, and two frameworks for feature extraction and benchmarking to facilitate reproducibility. Da-TACOS contains 25K songs represented by unique editorial metadata plus 9 low- and mid-level features pre-computed with open source libraries, and is divided into two subsets. The Cover Analysis subset contains audio features (e.g. key, tempo) that can serve to study how musical characteristics vary for cover songs. The Benchmark subset contains the set of features that have been frequently used in CSI research, e.g. chroma, MFCC, beat onsets etc. Moreover, we provide initial benchmarking results of a selected number of state-of-the-art CSI algorithms using our dataset, and for reproducibility, we share a GitHub repository containing the feature extraction and benchmarking frameworks. ; This work is partially supported by the MIP-Frontiers project, the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068, and by TROMPA, the Horizon 2020 project 770376-2.