MusicCaps is a dataset composed of 5.5k music-text pairs, with rich text descriptions provided by human experts. For each 10-second music clip, MusicCaps provides:
37 PAPERS • 1 BENCHMARK
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
7 PAPERS • 1 BENCHMARK
First large-scale symphony generation dataset.
For each dataset we provide a short description as well as some characterization metrics. It includes the number of instances (m), number of attributes (d), number of labels (q), cardinality (Card), density (Dens), diversity (Div), average Imbalance Ratio per label (avgIR), ratio of unconditionally dependent label pairs by chi-square test (rDep) and complexity, defined as m × q × d as in [Read 2010]. Cardinality measures the average number of labels associated with each instance, and density is defined as cardinality divided by the number of labels. Diversity represents the percentage of labelsets present in the dataset divided by the number of possible labelsets. The avgIR measures the average degree of imbalance of all labels, the greater avgIR, the greater the imbalance of the dataset. Finally, rDep measures the proportion of pairs of labels that are dependent at 99% confidence. A broader description of all the characterization metrics and the used partition methods are described in
4 PAPERS • NO BENCHMARKS YET
This dataset includes all music sources, background noises and impulse-reponses (IR) samples and conversation speech that have been used in the work "Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning" ICASSP 2021 (https://arxiv.org/abs/2010.11910).
2 PAPERS • NO BENCHMARKS YET
The YouTube8M-MusicTextClips dataset consists of over 4k high-quality human text descriptions of music found in video clips from the YouTube8M dataset.
JamALT is a revision of the JamendoLyrics dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
1 PAPER • 5 BENCHMARKS
The Song Describer Dataset (SDD) contains ~1.1k captions for 706 permissively licensed music recordings. It is designed for use in evaluation of models that address music-and-language (M&L) tasks such as music captioning, text-to-music generation and music-language retrieval.
1 PAPER • NO BENCHMARKS YET