Don’t hide in the frames: Note- and pattern-based evaluation of automated melody extraction algorithms

Abstract

optimized algorithms but not with the reference algorithm. Finally, the size of result sets of pattern similarity searches decreases for automated note extraction and for larger similarity thresholds but the difference levels out for smaller thresholds.

Multi-mmlg: a novel framework of extracting multiple main melodies from MIDI files

“…Audio-based main melody extraction only focuses on the former. Many conventional approaches estimate the pitch of main melody notes based on pitch salience and spectrogram [41][42][43][44][45]. These conventional methods are often complex and have many steps.…”

“…Audio-based methods Salamon [42], Choi [48], Wu [47], Lee [46] ----U Zhang [41], Paiva [45], Frieler [43] ---U -…”

The Jazz Ontology: A semantic model and large-scale RDF repositories for jazz

“…The work was concerned with automatic analysis of melodic patterns ("licks") in jazz improvisations, aiming to trace musical influence based on borrowing of licks [17]. The analysis workflow included automatic melody extraction from 50K audio recordings [18,19]; segmenting tracks into solo and other parts; and advanced pattern search on symbolic representations of solos [20]. This paper describes modelling, collecting, integrating, correcting, enriching metadata and linking it to audio.…”

“…LinkedJazz 16 is a research project at the Pratt Semantic Lab concerned with Linked Open Data, and in partic-200 ular documents and data related to the personal and professional lives of jazz artists [15,51,16]. The researchers crawled all the main Linked Open Data resources such as DBpedia, 17 Library of Congress authority files, 18 Mu-sicBrainz, 19 Virtual International Authority File, 20 to col-205 lect and link all entries related to jazz artists. The outcomes of automatic discovery (around 9,000 artists) were Figure 3: LinkedJazz properties describing relationships between jazz artists [52] curated by researchers and volunteers by means of a dedicated application.…”

Interval patterns are dependent on metrical position in jazz solos

“…In order to examine these patterns, we analyzed a subset of recordings of solos from the Weimar Jazz Database (WJD; Pfleiderer et al, 2017), a database of classic jazz solos that have been transcribed and digitized. This database has been used to test algorithms in recent research on the location of licks, the study of improvisation strategies (Frieler et al, 2018;Frieler, et al, 2019;Gómez et al, 2018;Gulz et al, 2019), and for corpus studies of note intensity, intonation, pitch, and note duration, among other musical elements, often looking for correlations between them, as we do in the present study (Abeßer et al, 2014;Abeßer et al, 2015). We used it to examine the relationship between licks and their metrical position.…”