AAM: a dataset of Artificial Audio Multitracks for diverse music information retrieval tasks

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dc.contributor.authorOstermann, Fabian
dc.contributor.authorVatolkin, Igor
dc.contributor.authorEbeling, Martin
dc.date.accessioned2024-03-22T13:59:21Z
dc.date.available2024-03-22T13:59:21Z
dc.date.issued2023-03-23
dc.description.abstractWe present a new dataset of 3000 artificial music tracks with rich annotations based on real instrument samples and generated by algorithmic composition with respect to music theory. Our collection provides ground truth onset information and has several advantages compared to many available datasets. It can be used to compare and optimize algorithms for various music information retrieval tasks like music segmentation, instrument recognition, source separation, onset detection, key and chord recognition, or tempo estimation. As the audio is perfectly aligned to original MIDIs, all annotations (onsets, pitches, instruments, keys, tempos, chords, beats, and segment boundaries) are absolutely precise. Because of that, specific scenarios can be addressed, for instance, detection of segment boundaries with instrument and key change only, or onset detection only in tracks with drums and slow tempo. This allows for the exhaustive evaluation and identification of individual weak points of algorithms. In contrast to datasets with commercial music, all audio tracks are freely available, allowing for extraction of own audio features. All music pieces are stored as single instrument audio tracks and a mix track, so that different augmentations and DSP effects can be applied to extend training sets and create individual mixes, e.g., for deep neural networks. In three case studies, we show how different algorithms and neural network models can be analyzed and compared for music segmentation, instrument recognition, and onset detection. In future, the dataset can be easily extended under consideration of specific demands to the composition process.en
dc.identifier.urihttp://hdl.handle.net/2003/42402
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-24238
dc.language.isoende
dc.relation.ispartofseriesEURASIP Journal on audio, speech, and music processing;2023
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de
dc.subjectArtificial music dataseten
dc.subjectMultitrack audio mixesen
dc.subjectAlgorithmic compositionen
dc.subjectMusic segmentationen
dc.subjectInstrument recognitionen
dc.subjectSource separationen
dc.subjectOnset detectionen
dc.subjectTempo estimationen
dc.subjectChord detectionen
dc.subject.ddc004
dc.subject.rswkDatensatzde
dc.subject.rswkKomposition <Musik>de
dc.titleAAM: a dataset of Artificial Audio Multitracks for diverse music information retrieval tasksen
dc.typeTextde
dc.type.publicationtypeResearchArticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationOstermann, F., Vatolkin, I. & Ebeling, M. AAM: a dataset of Artificial Audio Multitracks for diverse music information retrieval tasks. J AUDIO SPEECH MUSIC PROC. 2023, 13 (2023). https://doi.org/10.1186/s13636-023-00278-7de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1186/s13636-023-00278-7de

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