Archive for the ‘landscape’ Category

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The Landscape of music

December 6, 2009

From AT&T’s lab.  A nifty geographic representation of musical artist.  Zoom in and out to find artists.

Creator:  AT&T

Uses: GMAP – a technique for visualizing relations and structures as maps

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Using Visualizations for Music Discovery

October 22, 2009

Hot of the presses, here are the sides for the tutorial that Justin and Paul are presenting at ISMIR 2009 on October 26.

Note that the live presentation will include many demonstrations and videos of visualizations that just are not practical to include in a PDF.  If you have the chance, be sure to check out the tutorial at ISMIR in Kobe on the 26th.

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mHashup

September 8, 2009

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mHashup

Fast Visual Music Discovery Via Locality-Sensitive Hashing

mHashup is a novel visual interface to large music collections, such as today’s million-song download services, for discovering musical relationships among tracks. Users engage in direct on-screen query and retrieval of music fragments in an instantaneous feedback flow performed by a locality sensitive hash table in secondary storage.

mHashup facilitates both professional music searches (such as musicologists and copyright lawyers seeking the origins of sampled music with location markers precisely given for each returned track) and end-user music applications (such as discovery of “dark media” by its relationship to known “hot” items). The visual/auditory display of results incorporates summaries of retrieved tracks and facilitates a user-interaction feedback cycle for refining and expanding music discovery processes. mHashup’s visual interface uses the core functionality of a content-based search engine as a visual grammar to be explored by direct manipulation.

More Info:

Creators:

  • Michela Magas
  • Michael Casey
  • Christophe Rhode
  • Goldsmiths Digital Studios
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FM4 Soundpark

September 8, 2009

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soundpark

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The FM4 Soundpark is a web platform run by the Austrian public radio station FM4,  that visualizes an audio similarity music space.  Soundpark incorporates purely content-based rcommendations based upon a seed track and provides a 2D visualization based on audio similarity as well as an interactive 3D visualization based upon a combination of audio and metadata features.  Features of Soundpark:

  • Music Recommendation: The core of all applications is a content based music similarity function. The similarity is automatically computed and based on models of the songs’ audio content. Musical instruments and voices exhibit specific frequency patterns in the audio signal. These patterns are estimated with statistical models and used to compute the audio similarity.
  • Soundpark Player: Whenever a visitor of the Soundpark listens to a song, a recommendation of five or more similar songs is provided. These recommendations are visualized in a graph-based representation. Users can interactively explore the similarity space by clicking on songs in the recommendation graph.
  • Soundpark 3D: The entire database of songs in the Soundpark is visualized as an audio landscape of sea and islands. Songs from the same genre inhabit the same islands, within islands songs are grouped according to their audio similarity. Users can travel through the landscape and explore their own audio path through the data base.
  • Automatic generation of playlists: Visitors of the Soundpark can choose a start and an end song from the data base and a playlist of eight more songs “in-between” is automatically computed. The playlist is a smooth transition from the start to the end song. This functionality is not online any more.

More Info:

Creators:

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Databionic Music Miner

September 7, 2009

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The Databionic MusicMiner is a browser for music based on data mining techniques. You can create MusicMaps to visualize the similarity of songs and artists. Explore your music and create playlists based on the paradigm of geographical maps! Features include:

  • Automatic parsing of a folder tree with music files (MP3, OGG, WMA, M4A, MP2, WAV).
  • Automatic description of digital audio files by sound.
  • Creation of MusicMaps to navigate the sound space based on the paradigm of geographical maps.
  • Visual creation of playlists.
  • Similarity search in music collection based on sound.
  • Customizable hierarchichal browsing of the database by e.g. genre/artist/album or year/artist.
  • Flexible database including the seperate storage of several artists per song, albums and playlists as part of a playlist.
  • Import and export of meta information based on XML.

Creator:This system was developed by the Databionics Research Group at the University of Marburg, Germany. This group has released a number of open source tools that perform data mining tasks such as clustering, visualization and classification with Emergent Self-Organizing Maps. There’s a paper giving an overview of their toolkit here: ESOM-Maps: Tools for clustering, visualization, and classification with Emergent SOM

More Info


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nepTune

September 7, 2009

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nepTune_modes

Description

nepTune is an innovative user interface to music repositories. Given an arbitrary collection of digital music files, nepTune creates a virtual landscape which allows the user to freely navigate in this collection. This is accomplished by automatically extracting features from the audio signal and clustering the music pieces. The clustering is used to generate a 3D island landscape in which the user can freely navigate and hear the closest sounds with respect to his/her current position via a surround sound system. Additionally, knowledge extracted automatically from the Web is incorporated to enrich the landscape with semantic information. More precisely, nepTune displays words that describe the heard music and related images on the landscape to support the exploration.

Developed in 2006, 2007 by:

Knees, P., Schedl, M., Pohle, T., and Widmer, G from the Department of Computational Perception

Johannes Kepler Universität Linz

More Info