Music Information Retrieval (MIR) is an interdisciplinary research area that covers automated extraction of information from audio signals, music databases and services enabling the indexed information searching. In the early stages the primary focus of MIR was on music information through Query-by-Humming (QBH) applications, i.e. on identifying a piece of music by singing (singing/whistling), while more advanced implementations supporting Query-by-Example (QBE) searching resulted in names of audio tracks, song identifica-tion, etc. Both QBH and QBE required several steps, among others an optimized signal parametrization and the soft computing approach. Nowadays, MIR is associated with research based on the content analysis that is related to the retrieval of a musical style, genre or music referring to mood or emotions. Even though, this type of music retrieval called Query-by-Category still needs feature extraction and parametrization optimizing, but in this case search of global online music systems and services applications with their millions of users is based on statistical measures. The paper presents details concerning MIR back-ground and answers a question concerning usage of soft computing versus statistics, namely: why and when each of them should be employed. Keywords: Music Information Retrieval (MIR), feature extraction, soft computing, collaborative filtering (CF), similarity measures
Authors
Additional information
- Category
- Aktywność konferencyjna
- Type
- materiały konferencyjne indeksowane w Web of Science
- Language
- angielski
- Publication year
- 2015
