11,429 research outputs found
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
Generation of folk song melodies using Bayes transforms
The paper introduces the `Bayes transform', a mathematical procedure for putting data into a hierarchical representation. Applicable to any type of data, the procedure yields interesting results when applied to sequences. In this case, the representation obtained implicitly models the repetition hierarchy of the source. There are then natural applications to music. Derivation of Bayes transforms can be the means of determining the repetition hierarchy of note sequences (melodies) in an empirical and domain-general way. The paper investigates application of this approach to Folk Song, examining the results that can be obtained by treating such transforms as generative models
Wavelet-filtering of symbolic music representations for folk tune segmentation and classification
The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestaltbased method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients ’ local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and waveletfiltering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized. 1
09051 Abstracts Collection -- Knowledge representation for intelligent music processing
From the twenty-fifth to the thirtieth of January, 2009, the
Dagstuhl Seminar 09051 on ``Knowledge representation for intelligent music
processing\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Centre for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts
of the presentations and demos given during the seminar as well as
plenary presentations, reports of workshop discussions, results and
ideas are put together in this paper. The first section describes the
seminar topics and goals in general, followed by plenary `stimulus\u27
papers, followed by reports and abstracts arranged by workshop
followed finally by some concluding materials providing views of both
the seminar itself and also forward to the longer-term goals of the
discipline. Links to extended abstracts, full papers and supporting
materials are provided, if available.
The organisers thank David Lewis for editing these proceedings
Towards Automated Processing of Folk Song Recordings
Folk music is closely related to the musical culture of a
specific nation or region. Even though folk songs have been
passed down mainly by oral tradition, most musicologists study
the relation between folk songs on the basis of symbolic music
descriptions, which are obtained by transcribing recorded tunes
into a score-like representation. Due to the complexity of
audio recordings, once having the transcriptions, the original
recorded tunes are often no longer used in the actual folk song
research even though they still may contain valuable
information. In this paper, we present various techniques for
making audio recordings more easily accessible for music
researchers. In particular, we show how one can use
synchronization techniques to automatically segment and
annotate the recorded songs. The processed audio recordings can
then be made accessible along with a symbolic transcript by
means of suitable visualization, searching, and navigation
interfaces to assist folk song researchers to conduct large
scale investigations comprising the audio material
Detection of Melodic Patterns in Automatic Transcriptions of Flamenco Singing
The spontaneous expressive interpretation of melodic templates is a fundamental concept in flamenco music. Consequently, the automatic detection of such patterns in music collections sets the basis for a number of challenging analysis and retrieval tasks. We present a novel algorithm for the automatic detection of manually defined melodies within a corpus of automatic transcriptions of flamenco recordings. We evaluate the performance on the example of five characteristic patterns from the fandango de Valverde style and demonstrate that the algorithm is capable of retrieving ornamented instances of query patterns. Furthermore, we discuss limitations, possible extensions and applications of the proposed system
From speech to song: an interdisciplinary investigation of rhythm in English and Spanish
The general theoretical frame of this dissertation has to do with the study, from an
interdisciplinary and interlinguistic point of view, of the typological dichotomy
between stress-timed and syllable-timed languages, inasmuch as this distinction is
valid at all. As a preliminary step, I carry out a comparative examination of the basic
prosodic characteristics of English and Spanish, in order to then analyse the standard
versification systems of these two languages. In the central part of my dissertation, I
explore the most important text-setting Optimality Theory constraints as applied to a
corpus of English and Spanish folk and art songs.My main objective in carrying out these three-level analyses is to check
whether the actual setting of verse to music responds to some kind of underlying
rhythmic constraints common to language prosody, verse prosody and music, and
whether those constraints are ranked differently from language to language.The conclusions have to do with a correspondence between the timing
typologies of language and the rhythmic typologies of music. I find clear
inconsistencies or mismatches between speech prosody, on the one hand, and verse
and music rhythm, on the other. These inconsistencies work differently in a syllabletimed language like Spanish than in a stress-timed language like English. While in
the first type of languages I find a natural counterpoint or dialogue between speech
prosody and musical rhythm, in the second type this counterpoint tends to be
considered arhythmic. In other words, I establish a difference in kind in relation to
the dialogue between prosody and music for each of the two types of languages. In
English, the level of agreement between the two stress-patterns is really high, while
in Spanish the counterpoint between the two patterns is used as an expressive device
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