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AERFAI Summer School on Deep Learning

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Alicante July 5-6, 2017

10th international workshop on Machine Learning and Music

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Barcelona: 6th October 2017

Computer Music Laboratory

Automatic composition has been the area where computers have played a significant role traditionally, but now other areas related to human cognition have been introduced, like music categorization, performance, indexing and retrieval, music document analysis, perception of key, rhythm, meter, style, etc. In all these tasks pattern recognition and machine learning techniques have shown to be useful and that is our work domain.

Active lines of work

  • Symbolic music style recognition

    With applications like the indexation and exploration of music databases, genre analysis, or authoring. Statistical techniques are being applied to digital scores for this task.

  • Music encoding and cultural heritage

    Grammars and libraries for encoding and decoding different symbolic music languages. Optical music recognition. Hand-written recognition. Early music notations. Translation from old notations to modern music representations.
    "Plain and easie code" ANTLR v4 grammar
    Syntax diagrams
    MEI Customisation for Hierarchical Analysis

  • Automatic transcription of digital audio

    Extracting a symbolic representation (score) from musical signals. The polyphonic and polytimbral tasks are open problems in signal processing research. The role of human feedback in interactions and multimodality are being studied.

  • Automatic music analysis

    Melodic, harmonic, and functional analysis are powerful tools for music segmentation, key identification and tracking, music comparison, chordal progressions, reductions, performance rendering, etc.
    Additional information.

  • Track identification in multi-track MIDI files

    Automatically identifying the track containing a particular line or voice in a MIDI file, with straightforward applications in music retrieval and music comparison.

  • Music similarity metrics

    The key tool for music comparisons. Data structures suitable to represent music data and probabilistic apporaches are explored.

  • Algorithmic composition

    One of the classical tasks in computer music and artificial intelligence. Music representation and pattern recognition methods are used for style-guided composition through evolutionary methods.
    Paper in Music-AI Workshop, IJCAI 2007. Demos.

  • Digital sound synthesis

    Techniques for using computers to sound generation, involving computer languages like Csound. Control sequencies generation for performance rendering and music composition.

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