Design and Evaluation of a Probabilistic Music Projection Interface

We describe the design and evaluation of a probabilistic interface for music exploration and casual playlist generation. Predicted subjective features, such as mood and genre, inferred from low-level audio features create a 34- dimensional feature space. We use a nonlinear dimensionality reduction algorithm to create 2D music maps of tracks, and augment these with visualisations of probabilistic mappings of selected features and their uncertainty. We evaluated the system in a longitudinal trial in users’ homes over several weeks. Users said they had fun with the interface and liked the casual nature of the playlist generation. Users preferred to generate playlists from a local neighbourhood of the map, rather than from a trajectory, using neighbourhood selection more than three times more often than path selection. Probabilistic highlighting of subjective features led to more focused exploration in mouse activity logs, and 6 of 8 users said they preferred the probabilistic highlighting mode

Combining audio-based similarity with web-based data to accelerate automatic music playlist generation

We present a technique for combining audio signal-based music similarity with web-based musical artist similarity to accelerate the task of automatic playlist generation. We demonstrate the applicability of our proposed method by extending a recently published interface for music players that benefits from intelligent structuring of audio collections. While the original approach involves the calculation of similarities between every pair of songs in a collection, we incorporate web-based data to reduce the number of necessary similarity calculations. More precisely, we exploit artist similarity determined automatically by means of web retrieval to avoid similarity calculation between tracks of dissimilar and/or unrelated artists. We evaluate our acceleration technique on two audio collections with different characteristics. It turns out that the proposed combination of audio- and text-based similarity not only reduces the number of necessary calculations considerably but also yields better results, in terms of musical quality, than the initial approach based on audio data only. Additionally, we conducted a small user study that further confirms the quality of the resulting playlists

Interactive Exploration of Musical Space with Parametric t-SNE

(Abstract to follow

PlaySOM and PocketSOMPlayer, Alternative Interfaces to Large Music Collections

With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper we present the PlaySOM, as well as the PocketSOMPlayer, two novel interfaces that enable one to browse a music collection by navigating a map of clustered music tracks and to select regions of interest containing similar tracks for playing. The PlaySOM system is primarily designed to allow interaction via a large-screen device, whereas the PocketSOMPlayer is implemented for mobile devices, supporting both local as well as streamed audio replay. This approach offers content-based organization of music as an alternative to conventional navigation of audio archives, i.e. flat or hierarchical listings of music tracks that are sorted and filtered by meta information. Keywords: User Interaction, Music Collections, Information Discovery and Retrieval, Audio Clustering, Audio Interfaces, Mobile Devices

PlaySOM and PocketSOMPlayer: Alternative interfaces to large music collections

With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. In this paper we present the PlaySOM, as well as the PocketSOMPlayer, two novel interfaces that enable one to browse a music collection by navigating a map of clustered music tracks and to select regions of interest containing similar tracks for playing. The PlaySOM system is primarily designed to allow interaction via a large-screen device, whereas the PocketSOMPlayer is implemented for mobile devices, supporting both local as well as streamed audio replay. This approach offers content-based organization of music as an alternative to conventional navigation of audio archives, i.e. flat or hierarchical listings of music tracks that are sorted and filtered by meta information

Combining Metadata, Inferred Similarity of Content, and Human Interpretation for Managing and Listening to Music Collections

Music services, media players and managers provide support for content classification and access based on filtering metadata values, statistics of access and user ratings. This approach fails to capture characteristics of mood and personal history that are often the deciding factors when creating personal playlists and collections in music. This dissertation work presents MusicWiz, a music management environment that combines traditional metadata with spatial hypertext-based expression and automatically extracted characteristics of music to generate personalized associations among songs. MusicWiz’s similarity inference engine combines the personal expression in the workspace with assessments of similarity based on the artists, other metadata, lyrics and the audio signal to make suggestions and to generate playlists. An evaluation of MusicWiz with and without the workspace and suggestion capabilities showed significant differences for organizing and playlist creation tasks. The workspace features were more valuable for organizing tasks, while the suggestion features had more value for playlist creation activities

Navigating the space of your music

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2008.Includes bibliographical references (p. 121-124).Navigating increasingly large personal music libraries is commonplace. Yet most music browsers do not enable their users to explore their collections in a guided and manipulable fashion, often requiring them to have a specific target in mind. MusicBox is a new music browser that provides this interactive control by mapping a music collection into a two-dimensional space, applying principal components analysis (PCA) to a combination of contextual and content-based features of each of the musical tracks. The resulting map shows similar songs close together and dissimilar songs farther apart. MusicBox is fully interactive and highly flexible: users can add and remove features from the included feature list, with PCA recomputed on the fly to remap the data. MusicBox is also extensible; we invite other music researchers to contribute features to its PCA engine. A small user study has shown that MusicBox helps users to find music in their libraries, to discover new music, and to challenge their assumptions about relationships between types of music.by Anita Shen Lillie.S.M