28.02.16 - 04.03.16, Seminar 16092

Computational Music Structure Analysis

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivation

Music is a ubiquitous and vital part of the lives of billions of people worldwide. Musical creations and performances are among the most complex and intricate of our cultural artifacts, and the emotional power of music can touch us in surprising and profound ways. Music spans an enormous range of forms and styles, from simple, unaccompanied folk songs, to orchestral music and music for other large ensembles, to a minutely constructed piece of electronic music. In view of the rapid and sustained growth of digital music sharing and distribution, the development of computational methods to help users find and organize music information has become an important field of research in both industry and academia. This seminar is devoted to a research area known as music structure analysis, where the general objective is to uncover the patterns and relationships that govern the organization of notes, events, and sounds in music.

Our objectives for the seminar are as follows: first, we will critically review the state of the art in computational approaches to music structure analysis in order to identify the main limitations of existing methodologies, while outlining a roadmap for future developments based on the most pressing challenges in this field; second, we plan to trigger interdisciplinary discussions that leverage insights from fields as disparate as psychology, music theory, composition, signal processing, machine learning, and information sciences in addressing the specific challenges of understanding structural information in music; and, third, we shall explore novel applications of these technologies in music and multimedia retrieval, content creation, musicology, education, and human-computer interaction.

General questions and issues that will be addressed in this seminar include:

  • Understanding, modeling and representing structural ambiguity
  • Hierarchical models for short-term/long-term structures
  • Measuring relevance of different musical properties and structure principles
  • Learning/fusing robust and expressive mid-level music representations
  • Handling partial similarity and structure invariances
  • Developing taxonomies/ontologies for structure annotation
  • Understanding structure across periods, styles and traditions
  • Influence of agents (composer, performer, listener) on structure
  • Adaptive and user-centered interfaces for structure visualization
  • Applications to content-based music indexing and organization
  • Content-based retrieval and navigation of music collections

As a key result of this seminar, we hope to achieve some significant progress in better understanding, modeling, representing, extracting, and use of musical structures. In particular, we plan to contribute to further closing the gap between music theory and psychology, and the computational sciences.