01875nas a2200253   4500000000100000000000100001008004100002260001200043653002500055653002000080653002400100653002100124653001000145653001200155100001600167700002600183700001900209245009700228856008000325300001200405490000600417520118400423022001401607       2023                            d  c06/202310aAffective Annotation10aCloud Computing10aEmotion recognition10aMachine Learning10aMusic10aSpotify1 aP. Álvarez1 aJ. García de Quirós1 aS. Baldassarri00aRIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs  uhttps://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_16.pdf  a168-1810 v83 aThe music emotions can help to improve the personalization of services and contents offered by music streaming providers. Many research works based on the use of machine learning techniques have addressed the problem of recognising the music emotions during the last years. Nevertheless, the results obtained are only applied on small-size music repositories and do not consider what the users feel when they listen to the songs. These issues prevent the existing proposals to be integrated into the personalization mechanisms of the online music providers. In this paper, we present the RIADA infrastructure which is composed by a set of systems able to annotate emotionally the catalog of songs offered by Spotify based on the users’ perception. RIADA works with the Spotify playlist miner and data services to build emotion recognition models that can solve the open challenges previously mentioned. Machine learning algorithms, music information retrieval techniques, architectures for parallelization of applications and cloud computing have been combined to develop a complex result of engineering able to integrate the music emotions into the Spotify-based applications.  a1989-1660