Machine Annotation of Traditional Irish Dance Music

The work presented in this thesis is validated in experiments using 130 realworld field recordings of traditional music from sessions, classes, concerts and commercial recordings. Test audio includes solo and ensemble playing on a variety of instruments recorded in real-world settings such as noisy public sessions. Results are reported using standard measures from the field of information retrieval (IR) including accuracy, error, precision and recall and the system is compared to alternative approaches for CBMIR common in the literature

Tunepal: Searching a Digital Library of Traditional Music Scores

Purpose – This paper aims to describe the Tunepal project as an example of a music information retrieval (MIR) system that is having an impact on how musicians access, learn and play traditional Irish music around the world. Design/methodology/approach – This paper describes the functionality of the Tunepal system: consisting of the tune corpus, the web site tunepal.org and mobile apps supporting iOS and Android OS. Tunepal facilitates query-by-title and query-by-playing music (QBP) searches and allows a musician to retrieve and playback scores amongst other supported functions. Findings – Tunepal has been favorably received and musicians report that the system is being used in a variety of scenarios including archiving and the preparation of sleeve notes for commercial recordings. Tunepal has a growing user base in 25 countries. Originality/value – The comprehensive tune corpus (over 16,000 compositions), the query-by-playing technology and the fact that the mobile apps provide access to the corpus in situ in traditional music sessions and classes make this project uniquely useful

Key Inference from Irish Traditional Music Scores and Recordings

The aim of this paper is to present techniques and results for identifying the key of Irish traditional music melodies, or tunes. Several corpora are used, consisting of both symbolic and audio representations. Monophonic and heterophonic recordings are present in the audio datasets. Some particularities of Irish traditional music are discussed, notably its modal nature. New key-profiles are defined, that are better suited to Irish music

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

Rhythm Inference From Audio Recordings of Irish Traditional Music

A new method is proposed to infer rhythmic information from audio recordings of Irish traditional tunes. The method relies on he repetitive nature of this musical genre. Low-level spectral features and autocorrelation are used to obtain a low-dimensional representation, on which logistic regression models are trained. Two experiments are conducted to predict rhythmic information at different levels of precision. The method is tested on a collec- ion of session recordings, and high accuracy scores are reported. A new method is proposed to infer rhythmic information from audio recordings of Irish traditional tunes. The method relies on he repetitive nature of this musical genre. Low-level spectral features and autocorrelation are used to obtain a low-dimensional representation, on which logistic regression models are trained. Two experiments are conducted to predict rhythmic information at different levels of precision. The method is tested on a collection of session recordings, and high accuracy scores are reported

A system for automatically annotating traditional Irish music field recordings

This paper presents MATT2 (Machine Annotation of Traditional Tunes). MATT2 is a novel system which can automatically annotate field recordings of traditional Irish music with useful metadata such as tune name, key signature, time signature, composer and discography. MATT2 works by using a number of algorithms to automatically transcribe digital audio to be annotated to the ABC music notation language. It then compares these transcriptions against a corpus of 860 human made transcriptions in ABC using a variation of the edit distance algorithm. Results using MATT2 to annotate fifty recordings of flute and fiddle tunes demonstrate a high success rate at annotating recordings made by different musicians. Additionally, several of the recordings successfully annotated in testing MATT2 were recorded in imperfect conditions, with badly degraded audio

A Corpus of Annotated Irish Traditional Dance Music Recordings: Design and Benchmark Evaluations

An emerging trend in music information retrieval (MIR) is the use of supervised machine learning to train automatic music transcription models. A prerequisite of adopting a machine learning methodology is the availability of annotated corpora. However, different genres of music have different characteristics and modelling these characteristics is an important part of creating state of the art MIR systems. Consequently, although some music corpora are available the use of these corpora is tied to the specific music genre, instrument type and recording context the corpus covers. This paper introduces the first corpus of annotations of audio recordings of Irish traditional dance music that covers multiple instrument types and both solo studio and live session recordings. We first discuss the considerations that motivated our design choices in developing the corpus. We then benchmark a number of automatic music transcription algorithms against the corpus. The underlying dataset for this research is available here at Github or here in Arrow

A Search Through Time: Connecting Live Playing to Archive Recordings of Traditional Music

This poster describes new developments in the popular Tunepal project. Tunepal is a query-by-playing music score search engine used primarily by musicians on smartphones in traditional music sessions and classes. Using Tunepal, a musician can quickly identify the name of a melody being played and download the score for later study. Since 2009, there has also been a version of Tunepal that runs in a web browser that allows a musician to play a tune extract and find the name of the tune. Over the summer of 2015, we embarked on a project to redevelop the Tunepal website in HTML5. Additionally we aimed to connect Tunepal searches which normally return music scores, to recordings of those scores, through the Eurpoeana Sounds project. Finally, we aimed to make the core Tunepal technology open source and provide API access to the Tunepal corpus and search engine so that others could build on our work

Antimicrobial resistance in commensal faecal Escherichia coli of hospitalised horses

The objective of this study was to examine the impact of hospitalisation and antimicrobial drug administration on the prevalence of resistance in commensal faecal E. coli of horses. Faecal samples were collected from ten hospitalised horses treated with antimicrobials, ten hospitalised horses not treated with antimicrobials and nine non-hospitalised horses over a consecutive five day period and susceptibility testing was performed on isolated E. coli. Results revealed that hospitalisation alone was associated with increased prevalence of antimicrobial resistance and multidrug resistance in commensal E. coli of horses. Due to the risk of transfer of resistance between commensal and pathogenic bacteria, veterinarians need to be aware of possible resistance in commensal bacteria when treating hospitalised horses