From Music Ontology Towards Ethno-Music-Ontology

This paper presents exploratory work investigating the suitability of the Music Ontology - the most widely used formal specification of the music domain - for modelling non-Western musical traditions. Four contrasting case studies from a variety of musical cultures are analysed: Dutch folk song research, reconstructive performance of rural Russian traditions, contemporary performance and composition of Persian classical music, and recreational use of a personal world music collection. We propose semantic models describing the respective do- mains and examine the applications of the Music Ontology for these case studies: which concepts can be successfully reused, where they need adjustments, and which parts of the reality in these case studies are not covered by the Mu- sic Ontology. The variety of traditions, contexts and modelling goals covered by our case studies sheds light on the generality of the Music Ontology and on the limits of generalisation “for all musics” that could be aspired for on the Semantic Web

Agreement among human and annotated transcriptions of global songs

Cross-cultural musical analysis requires standardized symbolic representation of sounds such as score notation. However, transcription into notation is usually conducted manually by ear, which is time-consuming and subjective. Our aim is to evaluate the reliability of existing methods for transcribing songs from diverse societies. We had 3 experts independently transcribe a sample of 32 excerpts of traditional monophonic songs from around the world (half a cappella, half with instrumental accompaniment). 16 songs also had pre-existing transcriptions created by 3 different experts. We compared these human transcriptions against one another and against 10 automatic music transcription algorithms. We found that human transcriptions can be sufficiently reliable (~90% agreement, κ ~.7), but current automated methods are not (<60% agreement, κ <.4). No automated method clearly outperformed others, in contrast to our predictions. These results suggest that improving automated methods for cross-cultural music transcription is critical for diversifying MIR

Globally, songs and instrumental melodies are slower and higher and use more stable pitches than speech: A Registered Report

Both music and language are found in all known human societies, yet no studies have compared similarities and differences between song, speech, and instrumental music on a global scale. In this Registered Report, we analyzed two global datasets: (i) 300 annotated audio recordings representing matched sets of traditional songs, recited lyrics, conversational speech, and instrumental melodies from our 75 coauthors speaking 55 languages; and (ii) 418 previously published adult-directed song and speech recordings from 209 individuals speaking 16 languages. Of our six preregistered predictions, five were strongly supported: Relative to speech, songs use (i) higher pitch, (ii) slower temporal rate, and (iii) more stable pitches, while both songs and speech used similar (iv) pitch interval size and (v) timbral brightness. Exploratory analyses suggest that features vary along a “musi-linguistic” continuum when including instrumental melodies and recited lyrics. Our study provides strong empirical evidence of cross-cultural regularities in music and speech

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

Jazz is a musical tradition that is just over 100 years old; unlike in other Western musical traditions, improvisation plays a central role in jazz. Modelling the domain of jazz poses some ontological challenges due to specificities in musical content and performance practice, such as band lineup fluidity and importance of short melodic patterns for improvisation. This paper presents the Jazz Ontology – a semantic model that addresses these challenges. Additionally, the model also describes workflows for annotating recordings with melody transcriptions and for pattern search. The Jazz Ontology incorporates existing standards and ontologies such as FRBR and the Music Ontology. The ontology has been assessed by examining how well it supports describing and merging existing datasets and whether it facilitates novel discoveries in a music browsing application. The utility of the ontology is also demonstrated in a novel framework for managing jazz related music information. This involves the population of the Jazz Ontology with the metadata from large scale audio and bibliographic corpora (the Jazz Encyclopedia and the Jazz Discography). The resulting RDF datasets were merged and linked to existing Linked Open Data resources. These datasets are publicly available and are driving an online application that is being used by jazz researchers and music lovers for the systematic study of jazz

History of Recorded Jazz: DTL1000, 1920-2020

We present the DTL1000 dataset, which was created in the “Dig That Lick” project and covers the history of recorded jazz with a sample of 1,750 improvisations extracted from 1,060 audio tracks. The dataset contains a mixture of collected (editorial metadata), manually annotated (structure, style), and automatically generated (main melody transcriptions of solos) data describing the recordings. The motivation for creating this dataset was the study of patterns in jazz improvisation, but there are many other applications for this resource. The accompanying paper presents the dataset creation process, data structure and contents with descriptive statistics and discusses the origin and process of the annotations, as well as general use cases and specifically the case of pattern analysis. These components and their combinations enable a number of use cases for jazz studies as well as algorithm development for music analysis. The DTL1000 dataset provides a rich resource for a variety of disciplines, and constitutes a contribution to a field where large datasets with rich annotations are scarce.The recorded legacy of jazz spans a century and provides a vast corpus of data documenting its development. Recent advances in digital signal processing and data analysis technologies enable automatic recognition of musical structures and their linkage through metadata to historical and social context. Automatic metadata extraction and aggregation give unprecedented access to large collections, fostering new interdisciplinary research opportunities. This project aims to develop innovative technological and music-analytical methods to gain fresh insight into jazz history by bringing together renowned scholars and results from several high-profile projects. Musicologists and computer scientists will together create a deeper and more comprehensive understanding of jazz in its social and cultural context. We exemplify our methods via a full cycle of analysis of melodic patterns, or "licks", from audio recordings to an aesthetically contextualised and historically situated understanding.</p

Globally, songs and instrumental melodies are slower and higher and use more stable pitches than speech: A Registered Report

Both music and language are found in all known human societies, yet no studies have compared similarities and differences between song, speech, and instrumental music on a global scale. In this Registered Report, we analyzed two global datasets: (i) 300 annotated audio recordings representing matched sets of traditional songs, recited lyrics, conversational speech, and instrumental melodies from our 75 coauthors speaking 55 languages; and (ii) 418 previously published adult-directed song and speech recordings from 209 individuals speaking 16 languages. Of our six preregistered predictions, five were strongly supported: Relative to speech, songs use (i) higher pitch, (ii) slower temporal rate, and (iii) more stable pitches, while both songs and speech used similar (iv) pitch interval size and (v) timbral brightness. Exploratory analyses suggest that features vary along a “musi-linguistic” continuum when including instrumental melodies and recited lyrics. Our study provides strong empirical evidence of cross-cultural regularities in music and speech

Globally, songs and instrumental melodies are slower, higher, and use more stable pitches than speech: a registered report

Both music and language are found in all known human societies, yet no studies have compared similarities and differences between song, speech, and instrumental music on a global scale. In this Registered Report, we analyzed two global datasets: 1) 300 annotated audio recordings representing matched sets of traditional songs, recited lyrics, conversational speech, and instrumental melodies from our 75 coauthors speaking 55 languages; and 2) 418 previously published adult-directed song and speech recordings from 209 individuals speaking 16 languages. Of our six pre-registered predictions, five were strongly supported: relative to speech, songs use 1) higher pitch, 2) slower temporal rate, and 3) more stable pitches, while both songs and speech used similar 4) pitch interval size, and 5) timbral brightness. Exploratory analyses suggest that features vary along a “musi-linguistic” continuum when including instrumental melodies and recited lyrics. Our study provides strong empirical evidence of cross-cultural regularities in music and speech