MUHSIC: An Open Dataset with Temporal Musical Success Information

Oliveira, Gabriel P.; Barbosa, Gabriel R. G.; Melo, Bruna C.; Silva, Mariana O.; Seufitelli, Danilo B.; Moro, Mirella M.

doi:10.5753/dsw.2021.17415

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…Moreover, the success time series may be used for analyzing temporal evolution and identifying success trends in the music industry. This article extends a paper from the Dataset Showcase Workshop of the 36 th Brazilian Symposium on Databases [Oliveira et al 2021b]. Its main contribution is an extension of MUHSIC with success data from Brazil, called MUHSIC-BR.…”

Section: Introductionmentioning

confidence: 86%

“…For example, Time Series Analysis is useful for understanding and predicting artists' success according to one or multiple attributes [Janosov et al 2020]. Such applications are discussed in detail in the MUHSIC dataset paper [Oliveira et al 2021b].…”

Section: Other Potential Applicationsmentioning

confidence: 99%

“…Another limitation is that the data sources consider only mainstream and popular music, generalizing the information. All such limitations are better discussed in the dataset paper [Oliveira et al 2021b].…”

Section: Conclusion and Future Improvementsmentioning

confidence: 99%

See 2 more Smart Citations

Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses

Oliveira

Barbosa²,

Melo³

et al. 2022

jidm

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Music is not only a worldwide essential cultural industry but also one of the most dynamic. The increasing volume of complex music-related data defines new challenges and opportunities for extracting knowledge, benefiting not only different music segments but also the Music Information Retrieval research field. In this article, we assess musical success in the United States and Brazil, two of the biggest music markets in the world. We first introduce MUHSIC and MUHSIC-BR, two novel datasets with enhanced success information that combine chart-related data with acoustic metadata to describe the temporal evolution of musical careers. Then, we use such enriched and curated data to cluster artists according to their success level by considering their high-impact periods (hot streaks). Our results reveal three groups with distinct success behavior over time. Furthermore, Brazil and the US present specific music success patterns regarding artists and genres, reflecting the importance of analyzing regional markets individually.

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Section: Introductionmentioning

confidence: 86%

Section: Other Potential Applicationsmentioning

confidence: 99%

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Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses

Oliveira

Barbosa²,

Melo³

et al. 2022

jidm

View full text Add to dashboard Cite

show abstract

“…Among the all existing datasets, MUHSIC [Oliveira et al 2021], MUHSIC-BR [Oliveira et al 2022], and MGD [Oliveira et al 2020] are the ones most similar to ours, as they have all been developed within our research group. MUHSIC and MUHSIC-BR focus on temporal success, providing comprehensive information on the temporal success of artists, songs, and genres -which aligns with the objective of our dataset.…”

Section: Existing Music Datasetsmentioning

confidence: 99%

MGD+: An Enhanced Music Genre Dataset with Success-based Networks

Seufitelli,

Oliveira,

Silva

et al. 2023

Anais Do v Dataset Showcase Workshop (DSW 2023)

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Streaming platforms like Spotify have revolutionized music consumption, generating big volumes of data on hit songs. Such data serve as input to analyzing the music community and to the field of Music Information Retrieval. In this context, we present MGD+: an enhanced Music Genre Dataset with Success-based Networks. By combining Spotify chart data with acoustic metadata, we capture the evolution of musical careers. We further enhance the dataset with a genre-based collaboration network, represented as a graph, connecting artists through collaborations. MGD+ enables building success-based time series across several music markets, offers a friendly interface, and allows reproducibility; being a valuable tool for music-related tasks.

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“…• Study on collaboration profiles in success-based genre networks [Oliveira et al 2020b] (Best Paper Presentation Award at ISMIR 2020); • Application of the hot streak methodology in the US and Brazil [Barbosa et al 2021, Seufitelli et al 2022] (Best Paper Award at SBCM 2021); • MUHSIC and MUHSIC-BR, datasets with temporal information on musical success [Oliveira et al 2021a, Oliveira et al 2021b]; • MGD, a dataset with genre success information [Oliveira et al 2020c]; • Analyses on patterns in hit songs .…”

mentioning

confidence: 99%

Analyses of Musical Success based on Time, Genre and Collaboration

Oliveira,

Lacerda,

Moro

2023

Anais Estendidos Do XXXVIII Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2023)

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Music holds a significant position in global culture, as it is one of the world's most important and dynamic cultural forms. With the vast amount of music-related data available on the Web, new opportunities emerge for extracting knowledge and benefiting different music segments. In this work, we perform a data-driven analysis to investigate musical success from a genre-oriented perspective. Specifically, we model both artist and genre success timelines to detect and predict continuous periods with higher impact. We also build success-based genre collaboration networks to detect collaboration profiles directly related to success. Furthermore, we use data mining techniques to uncover exceptional genre patterns in the networks where the success deviates from the average. Our findings show that studying genre collaboration is a powerful way to assess musical success by describing similar behaviors within collaborative songs. Overall, our work contributes to both the academy and the music industry, as we shed light on the underlying factors of the science behind musical success.

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MUHSIC: An Open Dataset with Temporal Musical Success Information

Cited by 4 publications

References 8 publications

Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses

Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses

MGD+: An Enhanced Music Genre Dataset with Success-based Networks

Analyses of Musical Success based on Time, Genre and Collaboration

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