Affective Music Information Retrieval

Much of the appeal of music lies in its power to convey emotions/moods and to evoke them in listeners. In consequence, the past decade witnessed a growing interest in modeling emotions from musical signals in the music information retrieval (MIR) community. In this article, we present a novel generative approach to music emotion modeling, with a specific focus on the valence-arousal (VA) dimension model of emotion. The presented generative model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the subjectivity of emotion perception by the use of probability distributions. Specifically, it learns from the emotion annotations of multiple subjects a Gaussian mixture model in the VA space with prior constraints on the corresponding acoustic features of the training music pieces. Such a computational framework is technically sound, capable of learning in an online fashion, and thus applicable to a variety of applications, including user-independent (general) and user-dependent (personalized) emotion recognition and emotion-based music retrieval. We report evaluations of the aforementioned applications of AEG on a larger-scale emotion-annotated corpora, AMG1608, to demonstrate the effectiveness of AEG and to showcase how evaluations are conducted for research on emotion-based MIR. Directions of future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio

Musemo: Express Musical Emotion Based on Neural Network

Department of Urban and Environmental Engineering (Convergence of Science and Arts)Music elicits emotional responses, which enable people to empathize with the emotional states induced by music, experience changes in their current feelings, receive comfort, and relieve stress (Juslin & Laukka, 2004). Music emotion recognition (MER) is a field of research that extracts emotions from music through various systems and methods. Interest in this field is increasing as researchers try to use it for psychiatric purposes. In order to extract emotions from music, MER requires music and emotion labels for each music. Many MER studies use emotion labels created by non-music-specific psychologists such as Russell???s circumplex model of affects (Russell, 1980) and Ekman???s six basic emotions (Ekman, 1999). However, Zentner, Grandjean, and Scherer suggest that emotions commonly used in music are subdivided into specific areas, rather than spread across the entire spectrum of emotions (Zentner, Grandjean, & Scherer, 2008). Thus, existing MER studies have difficulties with the emotion labels that are not widely agreed through musicians and listeners. This study proposes a musical emotion recognition model ???Musemo??? that follows the Geneva emotion music scale proposed by music psychologists based on a convolution neural network. We evaluate the accuracy of the model by varying the length of music samples used as input of Musemo and achieved RMSE (root mean squared error) performance of up to 14.91%. Also, we examine the correlation among emotion labels by reducing the Musemo???s emotion output vector to two dimensions through principal component analysis. Consequently, we can get results that are similar to the study that Vuoskoski and Eerola analyzed for the Geneva emotion music scale (Vuoskoski & Eerola, 2011). We hope that this study could be expanded to inform treatments to comfort those in need of psychological empathy in modern society.clos

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

Multimodal music information processing and retrieval: survey and future challenges

Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years