Content Based Retrieval and Navigation of Music

Contents 1 Introduction ................................................... 3 2 Literature Review .............................................. 4 2.1 Navigation ............................................... 4 2.2 Navigation of audio ........................................ 4 2.3 Contentrepresentations ..................................... 5 2.3.1 The musical score ................................... 6 2.3.2 Performance data .................................... 6 2.3.3 Musical pitch contours ................................ 6 2.3.4 Alternative representations ............................ 7 2.3.5 The Fourier transform ................................ 7 2.4 Content based retrieval ..................................... 8 2.4.1 Retrieval of digital audio samples ....................... 8 2.4.2 Query by humming .................................. 8 2.4.3 Lemstrm and Laine ................................. 9 2.4.4 Image matching techni

Music ranking techniques evaluated

In a music retrieval system, a user presents a piece of music as a query and the system must identify from a corpus of performances other pieces with a similar melody. Several techniques have been proposed for matching such queries to stored music. In previous work, we found that local alignment, a technique derived from bioinformatics, was more effective than the n-gram methods derived from information retrieval; other researchers have reported success with n-grams, but have not compared against local alignment. In this paper we explore a broader range of n-gram techniques, and test them with both manual queries and queries automatically extracted from MIDI files. Our experiments show that n-gram matching techniques can be as effective as local alignment; one highly effective technique is to simply count the number of n-grams in common between the query and the stored piece of music. N-grams are particularly effective for short queries and manual queries, while local alignment is superior for automatic queries

Song classifications for dancing

A fundamental problem in music is to classify songs according to their rhythm. A rhythm is represented by a sequence of Quick (Q) and Slow (S) symbols, which correspond to the (relative) duration of notes, such that S=QQ. In this paper we present a linear algorithm for locating the maximum-length substring of a music text t that can be covered by a given rhythm r. An efficient algorithm to solve this problem, can then be used to find which rhythm, from a given set of such rhythms, covers the largest part of the music sequence under question, and thus best describes that sequence

Content-based music retrieval by acoustic query

Ph.DDOCTOR OF PHILOSOPH

Introduktion till informationsvetenskapen

Alkuteos: Tiedon tie : johdatus informaatiotutkimukseen (kääntänyt Katja Sandqvist)