Interactive Music Genre Exploration with Visualization and Mood Control

Abstract

Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre exploration. However, little work has studied how to improve users' understandability and acceptance of the novel items as well as support users to explore a new domain. In this paper, we investigate how two different visualizations and mood control influence the perceived control, informativeness and understandability of a music genre exploration tool, and further to improve the helpfulness for new music genre exploration. Specifically, we compare a bar chart visualization used by earlier work to a contour plot which allows users to compare their musical preferences with both the recommended tracks as well as the new genre. Mood control is implemented with two sliders to set a preferred mood on energy and valence features (that correlate with psychological mood dimensions). In the online user study, mood control was manipulated between subjects, and the visualizations were compared within subjects. During the study (N=102), we measured users' subjective perceptions, experiences and the interactions with the system. Our results show that the contour plot visualization is perceived more helpful to explore new genres than the bar chart visualization, as the contour plot is perceived to be more informative and understandable. Users spent significantly more time and used the mood control more in the contour plot than in the bar chart visualization. Overall, our results show that the contour plot visualization combined with mood control serves as the most helpful way for new music genre exploration, because the mood control is easier to understand and use when made transparent via an informative visualization.

Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation

“…EmoMTB provides a unique experience due to the set of the following features, which, to the best of our knowledge, do not appear in this combination in any other system: EmoMTB integrates tracks from LFM-2b [ 7 , 8 ], a recent large-scale dataset, allowing to cluster and present to users a collection of almost half a million music tracks. This number substantially exceeds collections supported by previous audiovisual music exploration interfaces [ 9 – 12 ]. Due to the track projection and clustering approach that takes into account both audio and genre features, music tracks in EmoMTB are placed in a 2-dimensional space that enables smooth music genre transition.…”

“…EmoMTB integrates tracks from LFM-2b [ 7 , 8 ], a recent large-scale dataset, allowing to cluster and present to users a collection of almost half a million music tracks. This number substantially exceeds collections supported by previous audiovisual music exploration interfaces [ 9 – 12 ].…”

“…The user can interact with the visualization and create playlists by drawing lines in the 2-dimensional t-SNE projection. Liang and Willemsen [ 12 ] propose an audiovisual interface to discover new music genres based on emotions. They use the energy and valence features retrieved through Spotify’s Audio Features & Analysis API and represent tracks within a contour plot visualization along those two features, which the user can interact with.…”

“…(5) The listener’s emotion is considered during the personalization of the recommendation lists and is integrated into the visualization. This aspect is absent from other audiovisual interfaces with the exception of [ 12 ], where mood information adjusts the recommendation. (6) The user navigates EmoMTB ’s visualization through their personal smartphone using a gamepad-like controller instead of the commonly used keyboard and mouse settings [ 5 , 10 – 12 , 14 ].…”

Visual Signatures for Music Mood and Timbre

“…Despite the fact that the most music visualization studies paid attention to music structure and pitch, there are still some pioneering works that aimed to visualize music mood. In [3], the music mood, analyzed and visualized in a 2D space, enables the user to better explore the music list with a specific mood. Similarly, in [4] the music mood is denoted as the Valence-Arousal value as 2D vectors, which is then mapped to a specific RGB value on a color wheel.…”