Research

A summary of all my research, papers and free software for traditional musicians.

My paper "Compensating for Expressivness in Queries to a Content Based Music Information Retrieval System" - Was awarded the best presentation prize at ICMC 2009, Montreal, Canada.

All Journal Articles Conference Papers (PR) Conference Papers (Non-PR)
Books Book Chapters Tech Reports Theses Others

2009

Bryan Duggan, "Machine Annotation of Traditional Irish Dance Music", PhD Thesis, Dublin Institute of Technology. (2009) |pdf| |abstract|
Estimates put the canon of traditional Irish dance tunes at at least seven thousand compositions. The literature attributes this to the geographic isolation of rural communities which developed their own repertoire of tunes. Musicians playing traditional music have a personal repertoire of up to one thousand tunes. Given this diversity, a common problem faced by musicians and ethnomusicologists is identifying tunes from recordings. This is evident even in the number of commercial recordings whose title is gan ainm (without name). The work presented in this PhD thesis attempts to solve this problem by developing a Content Based Music Information Retrieval (CBMIR) system adapted to the characteristics of traditional Irish music. The thesis includes a comprehensive review of the domain of traditional Irish music and presents three chapters of related work in the fields of feature extraction, melodic similarity and music information retrieval. A system is presented called MATT2 (Machine Annotation of Traditional Tunes) whose primary goal is to annotate recordings of traditional Irish dance music with useful metadata including tune names. MATT2 incorporates a number of novel algorithms for transcription of traditional music and for adapting melodic similarity measures to expressiveness in the playing of traditional music. It makes use of an onset detection function developed for the playing of traditional music on woodwind instruments such as the concert flute and tin-whistle. It uses a novel transcription algorithm based on Brendan Breathneach’s observations about the transcription of traditional Irish music which provides transposition invariance for the keys and modes used to play traditional music. It incorporates a new algorithm for dealing with ornamentation notes and accommodating "the long note" in traditional music called Ornamentation Filtering. It makes use of publicly available collections of traditional music available in the ABC notation. It uses a matching algorithm tolerant to errors which aligns short queries with longer strings from a corpus of known tunes, meaning that the algorithm can match entire tunes, incipits and phrases from any part of tune with equal success. The matching algorithm has also been adapted to take account of phrasing and reversing effects. A new algorithm is presented called TANSEY (Turn ANnotation from SEts using SimilaritY profiles) which annotates sets of tunes played segue as is the custom in traditional Irish dance music. The work presented in this thesis is validated in experiments using 130 real-world 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.
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Bryan Duggan, Brendan O'Shea, Mikel Gainza, Pádraig Cunningham, "Compensating for Expressiveness in Queries to a Content Based Music Information System", International Computer Music Conference (ICMC 2009), Montreal, Canada 16–21 August. (2009) |pdf| |abstract|
MATT2 is a content based music information retrieval system adapted to the characteristics of traditional Irish dance music. MATT2 compensates for expressive artefacts commonly employed by traditional musicians. Specifically these are ornamentation, "the long note", reversing and phrasing. In this paper we describe the main components of MATT2 and present an experiment where we demonstrate that using this higher level knowledge of melodic similarity in traditional Irish dance music results in a significant improvement in annotation accuracy over standard approaches from the MIR literature.
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2008

Bryan Duggan, Brendan O'Shea, Mikel Gainza, Padraig Cunningham, "The Annotation of Traditional Irish Dance Music using MATT2 and TANSEY", The 8th Annual Information Technology & Telecommunication Conference, Galway Mayo Institute of Technology, Galway, Ireland, October 2008. (2008) |pdf| |abstract|
Currents estimates put the cannon of traditional Irish dance tunes at least 7,000 compositions. Given this diversity, a common problem faced by musicians and ethnomusicologists is identifying tunes from recordings. This is evident even in the number of commercial recordings whose title is gan ainm (without name). This work attempts to solve this problem by developing a Content Based Music Information Retrieval (CBMIR) System adapted to the characteristics of traditional Irish music. A system is presented called MATT2 (Machine Annotation of Traditional Tunes) whose primary goal is to annotate recordings of traditional Irish dance music with useful meta-data including tune names. MATT2 incorporates a number of novel algorithms for transcription of traditional music and for adapting melodic similarity measures to the creativity and style present in the playing of traditional music. It incorporates a new algorithm for filtering ornamentation notes and accommodating "the long note" in traditional music called Ornamentation Filtering using Adaptive Histograms (OFAH). A new algorithm is presented called TANSEY (Turn ANnotation from SEts using SimilaritY profiles) that annotates sets of tunes played segue as is the custom in traditional Irish dance music. The work presented is validated in experiments using 130 real-world 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 measure 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.
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B. Duggan, B. O'Shea, and P. Cunningham, "A System for Automatically Annotating Traditional Irish Music Field Recordings", The Sixth International Workshop on Content-Based Multimedia Indexing, Queen Mary University of London, UK, Jun. 2008. (2008) |pdf| |abstract|
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.
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B. Duggan, B. O'Shea, Mikel Gainza and P. Cunningham, "Machine Annotation of Sets of Traditional Irish Dance Tunes", The Ninth International Conference on Music Information Retrieval (ISMIR), Drexel University, Philadelphia, USA, September 2008. (2008) |pdf| |abstract|
A set in traditional Irish music is a sequence of two or more dance tunes in the same time signature, where each tune is repeated an arbitrary number of times. A turn in a set represents the point at which either a tune repeats or a new tune is introduced. Tunes in sets are played in a segue (without a pause) and so detecting the turn is a significant challenge. This paper presents the MATS algorithm, a novel algorithm for identifying turns in sets of traditional Irish music. MATS works on digitised audio files of monophonic flute and tin-whistle music. Previous work on machine annotation of traditional music is summarised and experimental results validating the MATS algorithm are presented.
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2007

Duggan, B, "TunePal: A Portable Tune Teaching Tool for Traditional Musicians", DIT Annual Showcase of Learning & Teaching Activities, January, 2007. (2007) |pdf|
Pan Liqiang, Bryan Duggan, Ronan Fitzpatrick, "Experiences Teaching Website Engagibility to Computer Science Students", The Third China-Europe International Symposium on Software Industry-Oriented Education (CEIS-SIOE 2007), Dublin Castle, Dublin, Ireland February 6-7, 2007. (2007) |pdf|
Bryan Duggan, "Enabling Access to Irish Traditional Music Archives on a PDA", Eight Annual Irish Educational Technology Users Conference, DIT Bolton St. Ireland, May 23rd - May 25th. (2007) |pdf|
Zheng N, Duggan, B, "A Combinational Creativity Approach to Composing Traditional Irish Reels", 18th Irish Conference on Artificial Intelligence and Cognitive Science 29th - 31st August 2007, Dublin Institute of Technology. (2007) |pdf|

2006

Mhedi, Q., Mtenzi, F., Duggan, B., Mcatamney, F., "Proceedings of 9th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games (Editors)", 22-24 November 2006, Dublin Institute of Technology, Dublin, Ireland. (Editor). (2006)
Duggan, B., "Learning Traditional irish Music using a PDA", IADIS Mobile Learning Conference, Trinity College, Dublin, Ireland, July 2006. (2006) |pdf|
Duggan, B., Mtenzi, F., "An Optimised Implementation of the A Star Algorithm using the STL", 8th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games, 24th-26th July, Louisville, Kentucky, USA. (2006) |pdf|
Duggan, B., Zheng, C., Cunningham, P., "MATT - A System for Modelling Creativity in Traditional Irish Flute Playing", Third Joint Workshop on Computational Creativity, ECAI'06, Italy, August 2006. (2006) |pdf|
Richie, A., Lindstrum, P., Duggan, B, "Using the Source engine for Serious Games,", 9th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games 22-24 November 2006, Dublin Institute of Technology, Dublin, Ireland. (2006) |pdf|

2005

McAtamney, H., Duggan, B. and Mtenzi, FJ., "Using the Crytek Game Engine in the Dublin Institute of Technology", 7th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games 28-30 November 2005 CNBDI at Magelis, Angoulême, France. (2005) |PDF|
Duggan, B, McAtamney, H. and Mtenzi, FJ., "Learning Games Programming with "Dalek World"", 7th International Conference on Computer Games: AI, Animation, Mobile, Educational & Serious Games 28-30 November 2005 CNBDI at Magelis, Angoulême, France. (2005) |pdf|

2003

Bryan Duggan, "Revenue Opportunities in the Voice Enabled Web", School of Computing Report. (2003) |pdf|
Bryan Duggan, "Strategies for Enterprise Voice Enabled Web Projects", MSc Thesis. (2003) |pdf|
Bryan Duggan, "The Talking Internet", NCI Alumni Magazine, Issue 1. (2003) |pdf|
Duggan, B. and Deegan, M., "Considerations in the usage of Text To Speech in the Creation of Natural Sounding Voice Enabled Web Systems", International Symposium on Information and Communication Technologies, Trinity College Dublin September 24-26, 2003. (2003) |pdf|

2002

Bryan Duggan, "Considerations in the Usage of Speech Technology for Telephony Applications", School of Computing Technical Report. (2002) |pdf|