3,061 research outputs found
Integrating Segmentation and Similarity in Melodic Analysis
The recognition of melodic structure depends on both the segmentation into structural units, the melodic motifs, and relations of motifs which are mainly determined by similarity. Existing models and studies of segmentation and motivic similarity cover only certain aspects and do not provide a comprehensive or coherent theory. In this paper an Integrated Segmentation and Similarity Model (ISSM) for melodic analysis is introduced. The ISSM yields an interpretation similar to a paradigmatic analysis for a given melody. An interpretation comprises a segmentation, assignments of related motifs and notes, and detailed information on the differences of assigned motifs and notes. The ISSM is based on generating and rating interpretations to find the most adequate one. For this rating a neuro-fuzzy-system is used, which combines knowledge with learning from data. The ISSM is an extension of a system for rhythm analysis. This paper covers the model structure and the features relevant for melodic and motivic analysis. Melodic segmentation and similarity ratings are described and results of a small experiment which show that the ISSM can learn structural interpretations from data and that integrating similarity improves segmentation performance of the model
A Memetic Analysis of a Phrase by Beethoven: Calvinian Perspectives on Similarity and Lexicon-Abstraction
This article discusses some general issues arising from the study of similarity in music, both human-conducted and computer-aided, and then progresses to a consideration of similarity relationships between patterns in a phrase by Beethoven, from the first movement of the Piano Sonata in A flat major op. 110 (1821), and various potential memetic precursors. This analysis is followed by a consideration of how the kinds of similarity identified in the Beethoven phrase might be understood in psychological/conceptual and then neurobiological terms, the latter by means of William Calvin’s Hexagonal Cloning Theory. This theory offers a mechanism for the operation of David Cope’s concept of the lexicon, conceived here as a museme allele-class. I conclude by attempting to correlate and map the various spaces within which memetic replication occurs
Predictive uncertainty in auditory sequence processing
Copyright © 2014 Hansen and Pearce. This is an open-access article distributed under
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which does not comply with these terms
Infants segment words from songs - an EEG study
Children’s songs are omnipresent and highly attractive stimuli in infants’ input. Previous work suggests that infants process linguistic–phonetic information from simplified sung melodies. The present study investigated whether infants learn words from ecologically valid children’s songs. Testing 40 Dutch-learning 10-month-olds in a familiarization-then-test electroencephalography (EEG) paradigm, this study asked whether infants can segment repeated target words embedded in songs during familiarization and subsequently recognize those words in continuous speech in the test phase. To replicate previous speech work and compare segmentation across modalities, infants participated in both song and speech sessions. Results showed a positive event-related potential (ERP) familiarity effect to the final compared to the first target occurrences during both song and speech familiarization. No evidence was found for word recognition in the test phase following either song or speech. Comparisons across the stimuli of the present and a comparable previous study suggested that acoustic prominence and speech rate may have contributed to the polarity of the ERP familiarity effect and its absence in the test phase. Overall, the present study provides evidence that 10-month-old infants can segment words embedded in songs, and it raises questions about the acoustic and other factors that enable or hinder infant word segmentation from songs and speech
Prosodic description: An introduction for fieldworkers
This article provides an introductory tutorial on prosodic features such as tone and accent for researchers working on little-known languages. It specifically addresses the needs of non-specialists and thus does not presuppose knowledge of the phonetics and phonology of prosodic features. Instead, it intends to introduce the uninitiated reader to a field often shied away from because of its (in part real, but in part also just imagined) complexities. It consists of a concise overview of the basic phonetic phenomena (section 2) and the major categories and problems of their functional and phonological analysis (sections 3 and 4). Section 5 gives practical advice for documenting and analyzing prosodic features in the field.National Foreign Language Resource Cente
Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency
accepteddate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfdate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfWe present Tony, a software tool for the interactive an- notation of melodies from monophonic audio recordings, and evaluate its usability and the accuracy of its note extraction method. The scientific study of acoustic performances of melodies, whether sung or played, requires the accurate transcription of notes and pitches. To achieve the desired transcription accuracy for a particular application, researchers manually correct results obtained by automatic methods. Tony is an interactive tool directly aimed at making this correction task efficient. It provides (a) state-of-the art algorithms for pitch and note estimation, (b) visual and auditory feedback for easy error-spotting, (c) an intelligent graphical user interface through which the user can rapidly correct estimation errors, (d) extensive export functions enabling further processing in other applications. We show that Tony’s built in automatic note transcription method compares favourably with existing tools. We report how long it takes to annotate recordings on a set of 96 solo vocal recordings and study the effect of piece, the number of edits made and the annotator’s increasing mastery of the software. Tony is Open Source software, with source code and compiled binaries for Windows, Mac OS X and Linux available from https://code.soundsoftware.ac.uk/projects/tony/
Melodic Transcription of Flamenco Singing from Monophonic and Polyphonic Music Recordings
We propose a method for the automatic transcription of flamenco singing from monophonic and
polyphonic music recordings. Our transcription system is based on estimating the fundamental frequency (f0)
of the singing voice, and follows an iterative strategy for note segmentation and labelling. The generated
transcriptions are used in the context of melodic similarity, style classification and pattern detection. In our
study, we discuss the difficulties found in transcribing flamenco singing and in evaluating the obtained
transcriptions, we analyze the influence of the different steps of the algorithm, and we state the main
limitations of our approach and discuss the challenges for future studies
Data-driven, memory-based computational models of human segmentation of musical melody
When listening to a piece of music, listeners often identify distinct sections or segments
within the piece. Music segmentation is recognised as an important process in the abstraction
of musical contents and researchers have attempted to explain how listeners
perceive and identify the boundaries of these segments.The present study seeks the development of a system that is capable of performing
melodic segmentation in an unsupervised way, by learning from non-annotated musical
data. Probabilistic learning methods have been widely used to acquire regularities in
large sets of data, with many successful applications in language and speech processing.
Some of these applications have found their counterparts in music research and have
been used for music prediction and generation, music retrieval or music analysis, but
seldom to model perceptual and cognitive aspects of music listening.We present some preliminary experiments on melodic segmentation, which highlight
the importance of memory and the role of learning in music listening. These experiments
have motivated the development of a computational model for melodic segmentation
based on a probabilistic learning paradigm.The model uses a Mixed-memory Markov Model to estimate sequence probabilities
from pitch and time-based parametric descriptions of melodic data. We follow the assumption
that listeners' perception of feature salience in melodies is strongly related
to expectation. Moreover, we conjecture that outstanding entropy variations of certain
melodic features coincide with segmentation boundaries as indicated by listeners.Model segmentation predictions are compared with results of a listening study on
melodic segmentation carried out with real listeners. Overall results show that changes
in prediction entropy along the pieces exhibit significant correspondence with the listeners'
segmentation boundaries.Although the model relies only on information theoretic principles to make predictions
on the location of segmentation boundaries, it was found that most predicted segments
can be matched with boundaries of groupings usually attributed to Gestalt rules.These results question previous research supporting a separation between learningbased
and innate bottom-up processes of melodic grouping, and suggesting that some
of these latter processes can emerge from acquired regularities in melodic data
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
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