DOI: 10.5281/zenodo.5651429
title: Multi-modal dataset for music genre recognition based on six different modalities for LMD-aligned and SLAC datasets

description: Multi-modal dataset for music genre recognition based on six different modalities for the LMD-aligned [1] and SLAC [2] datasets. Further details are provided in [3]. <strong>Descriptions of files</strong> Link Description LMD-aligned_Filelist.arff File list with 1575 music tracks selected from the LMD-aligned dataset [1] with tagtraum genre annotations [4] (only a subset of LMD-aligned is used, which includes only pieces for which all six modalities were accessible, and which includes only well-represented genres) LMD-aligned_ExtractedFeatures.tar.gz Raw audio signal and model-based features extracted with AMUSE [5] LMD-aligned_ProcessedFeatures.tar.gz Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames LMD-aligned_Datasets.tar.gz Training, optimization, and test datasets for 3 splits for the recognition of 5 genres in [3] SLAC_Filelist.arff File list with 250 music tracks from the SLAC dataset [2] (genres and sub-genres are provided in the folder structure) SLAC_ExtractedFeatures.tar.gz Raw audio signal and model-based features extracted with AMUSE [5] SLAC_ProcessedFeatures.tar.gz Processed features: audio signal and model-based features aggregated for 4 s time frames with 2 s step size / all other features (see the table below) with the same values for all time frames SLAC_Datasets.tar.gz Training, optimization, and test datasets for 3 splits for the recognition of 5 genres and 10 sub-genres in [3] <strong>Modalities and feature sub-groups</strong> Modality Sub-group Dimensions in processed features of LMD-aligned Dimensions in processed features of SLAC Audio signal Low-level 1-524 1-524 Audio signal Semantic 525-810 525-810 Audio signal Structural complexity 811-908 811-908 Model-based Instruments 909-1018 909-1018 Model-based Moods 1019-1146 1019-1146 Model-based Various 1147-1402 1147-1402 Playlists Genres 1403-1973 1403-1973 Playlists Styles 1974-1695 1974-1695 Symbolic Pitch 1696-1757 1696-1757 Symbolic Melodic 1758-1781 1758-1781 Symbolic Chords 1782-1836 1782-1836 Symbolic Rhythm 1837-1935 1837-1935 Symbolic Tempo 1936-1963 1936-1963 Symbolic Instrument presence 1964-2441 1964-2441 Symbolic Instruments 2442-2456 2442-2456 Symbolic Texture 2457-2480 2457-2480 Symbolic Dynamics 2481-2484 2481-2484 Album covers SIFT 2485-2584 2485-2584 Lyrics jLyrics descriptors 2585-2603 2585-2671 Lyrics Bag-of-Words 2604-2703 Lyrics Doc2Vec 2704-2803
description: {"references": ["[1] Raffel, C. (2016). Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching. PhD thesis, Graduate School of Arts and Sciences, Columbia University", "[2] McKay, C., Burgoyne, J. A., Hockman, J., Smith, J. B. L., Vigliensoni, G., and Fujinaga, I. (2010). Evaluating the genre classification performance of lyrical features relative to audio, symbolic and cultural features. In Proc. of the 11th International Society for Music Information Retrieval Conference, ISMIR, pp. 213\u2013218", "[3] Vatolkin, I. and McKay, C. (2022). Multi-Objective Investigation of Six Feature Source Types for Multi-Modal Music Classification. Transactions of the International Society for Music Information Retrieval, 5(1), pp.1\u201319", "[4] Schreiber, H. (2015). Improving genre annotations for the million song dataset. In Proceedings of the 16th International Society for Music Information Retrieval Conference, ISMIR, pp. 241\u2013247", "[5] Vatolkin, I., Theimer, W. M., and Botteck, M. (2010). AMUSE (advanced music explorer) - A multitool framework for music data analysis. In Proc. of the 11th International Society for Music Information Retrieval Conference, ISMIR, pp. 33\u201338"]}