371 research outputs found
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
Music Information Retrieval for Irish Traditional Music Automatic Analysis of Harmonic, Rhythmic, and Melodic Features for Efficient Key-Invariant Tune Recognition
Music making and listening practices increasingly rely on techno logy,and,asaconsequence,techniquesdevelopedinmusicinformation retrieval (MIR) research are more readily available to end users, in par ticular via online tools and smartphone apps. However, the majority of MIRresearchfocusesonWesternpopandclassicalmusic,andthusdoes not address specificities of other musical idioms. Irishtraditionalmusic(ITM)ispopularacrosstheglobe,withregular sessionsorganisedonallcontinents. ITMisadistinctivemusicalidiom, particularly in terms of heterophony and modality, and these character istics can constitute challenges for existing MIR algorithms. The bene fitsofdevelopingMIRmethodsspecificallytailoredtoITMisevidenced by Tunepal, a query-by-playing tool that has become popular among ITM practitioners since its release in 2009. As of today, Tunepal is the state of the art for tune recognition in ITM. The research in this thesis addresses existing limitations of Tunepal. The main goal is to find solutions to add key-invariance to the tune re cognitionsystem,animportantfeaturethatiscurrentlymissinginTune pal. Techniques from digital signal processing and machine learning are used and adapted to the specificities of ITM to extract harmonic iv and temporal features, respectively with improvements on existing key detection methods, and a novel method for rhythm classification. These featuresarethenusedtodevelopakey-invarianttunerecognitionsystem that is computationally efficient while maintaining retrieval accuracy to a comparable level to that of the existing system
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
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
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 Similarity Matrix for Irish Traditional Dance Music
It is estimated that there are between seven and ten thousand Irish traditional dance tunes in existence. As Irish musicians travelled the world they carried their repertoire in their memories and rarely recorded these pieces in writing. When the music was passed down from generation to generation by ear the names of these pieces of music and the melodies themselves were forgotten or changed over time. This has led to problems for musicians and archivists when identifying the names of traditional Irish tunes.
Almost all of this music is now available in ABC notation from online collections. An ABC file is a text file containing a transcription of one or more melodies, the tune title, musical key, time signature and other relevant details.
The principal aim of this project is to define a process by which Irish music can be compared using string distance algorithms. An online survey will then be conducted to assess if human participants agree with the computer comparisons. Improvements will then be made to the string distance algorithms by considering music theory. Two other methods of assessing musical similarity, Breandán Breathnach‟s Melodic Indexing System and Parsons Code will be computerised and integrated into a Combined Ranking System (CRS). An hypothesis will be formed based on the results and experiences of creating this system. This hypothesis will be tested on humans and if successful, used to achieve the final aim of the project, to construct a similarity matrix
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
Improved onset detection for traditional flute recordings using convolutional neural networks
The usage of ornaments is key attribute that defines the style of
a flute performances within the genre of Irish Traditional Music
(ITM). Automated analysis of ornaments in ITM would allow for
the musicological investigation of a player’s style and would be
a useful feature in the analysis of trends within large corpora of
ITM music. As ornament onsets are short and subtle variations
within an analysed signal, they are substantially more difficult to
detect than longer notes. This paper addresses the topic of onset
detection for notes, ornaments and breaths in ITM. We propose
a new onset detection method based on a convolutional neural
network (CNN) trained solely on flute recordings of ITM. The
presented method is evaluated alongside a state-of-the-art gen
eralised onset detection method using a corpus of 79 full-length
solo flute recordings. The results demonstrate that the proposed
system outperforms the generalised system over a range of musi
cal patterns idiomatic of the genre
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