Department of Computational Perception
Department of
Computational Perception
 Johannes Kepler Universität Linz

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MUSIC INTERFACES & VISUALIZATION

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Personal music collection keep on growing and soon basically all music is going to be available to anyone – anywhere and anytime. Thus, the way music is used changes rapidly. However, the way music collections are organized and made accessible has basically remained the same, i.e., according to the hierarchical scheme (genre –) artist – album – track. Our goal is to overcome this limited view on music by building new graphical interfaces to visualize and to navigate in large collections. In case you are interested in visualization of or interfaces to other types of data, we are open to your ideas.

Intelligent Music Interfaces

The general philosophy is that music collections should be structured (automatically, by the computer) and presented according to intuitive musical criteria. Objectives are to develop innovative, creative, appealing, user-centered, and playful applications to access music and thus to enable new ways of discovering hidden treasures in large collections. One example is nepTune, an interactive, landscape-like interface that permits and even encourages the exploration of music repositories.

Examples for projects:
  • Planet nepTune: a Google Earth-like 3D user interface for browsing large music collections
  • Visual playlist generation
  • Exploring music collections in Virtual Reality environments (in cooperation with VRC)
  • Automatic Tagging and Organizing Music Collections using Audio Fingerprinting Technology
  • User-Adaptive User Interfaces to Music Collections
  • Personalized Music Player (stand-alone or plugin for iTunes, WinAmp, Banshee, AmaroK, or similar)
  • 3D multimedia user interface to browse collections of Web pages
  • Active music listening interfaces
  • Gesture and interaction-based interfaces
Contact: Peter Knees, Markus Schedl

Mobile Music Interfaces & Processing

For mobile devices the challenge is to provide powerful and efficient algorithms and strategies to deal with the limited resources available on such devices (e.g., processing power, screen resolution, interaction capabilities). Music processing on such devices includes developing optimized feature extractors and efficient usage of Web services.  Intelligent interfaces aim at elaborating novel and easy-to-use paradigms to improve the user experience when sifting through music and multimedia collections stored on the device or streamed from the Web.

Examples for projects (can be carried out on Android and iOS platforms alike):
  • Efficient implementation of acoustic features
  • 3D user interfaces to multimedia collections
  • Data extractors and user profiling
  • User-aware interfaces
  • Accelerometer-based activity detection
  • nepTune on Tablets
  • Implementing sound/tracks on mobile platforms
Contact: Markus Schedl, Peter Knees

Visualizations

We are also interested in and are actively developing various visualization approaches for different application scenarios related to information visualization and visual analytics. With our CoMIRVA framework we aim at developing a collection of Java-implementations of various algorithms concerning music, multimedia, information retrieval, information visualization, and data mining. In this context, various directions to include different visualization techniques are possible. Projects can be developed within the CoMIRVA framework, but also in programming languages other than Java, if desired.


Examples for projects:
  • Visualizing "Popularity Flows" around the world using Twitter data
  • Real-Time Music Visualisation
  • Investigating the wealth of dimensionality reduction/data projection techniques (e.g., spectral, graph-based, probabilistic methods, neural networks) for representing music and multimedia collections
  • Probabilistic Latent Variable Models to visualize large music collections
  • Extracting and visualizing relations from music-related Web pages (band members, albums, songs, ...)
  • A Web service API for CoMIRVA
  • Improving the performance of CoMIRVA: e.g. faster implementation of a data matrix, evaluating libraries for numeric mathematics
  • Implementation of a statistical data editor and an intuitive color map editor for CoMIRVA
Contact: Markus Schedl


last edited by pk on 2012-02-20