TY - JOUR KW - Affective Annotation KW - Cloud Computing KW - Emotion recognition KW - Machine Learning KW - Music KW - Spotify AU - P. Álvarez AU - J. García de Quirós AU - S. Baldassarri AB - The 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. IS - Regular Issue M1 - 2 N2 - The 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. PY - 2023 SE - 168 SP - 168 EP - 181 T2 - International Journal of Interactive Multimedia and Artificial Intelligence TI - RIADA: A Machine-Learning Based Infrastructure for Recognising the Emotions of Spotify Songs UR - https://www.ijimai.org/journal/sites/default/files/2023-05/ijimai8_2_16.pdf VL - 8 SN - 1989-1660 ER -