255 research outputs found
Bertso transformation with pattern-based sampling
This paper presents a method to generate new melodies, based on conserving the semiotic structure of a template piece. A pattern discovery algorithm is applied to a template piece to extract significant segments: those that are repeated and those that are transposed in the piece. Two strategies are combined to describe the semiotic coherence structure of the template piece: inter-segment coherence and intra-segment coherence. Once the structure is described it is used as a template for new musical content that is generated using a statistical model created from a corpus of bertso melodies and iteratively improved using a stochastic optimization method. Results show that the method presented here effectively describes a coherence structure of a piece by discovering repetition and transposition relations between segments, and also by representing the relations among notes within the segments. For bertso generation the method correctly conserves all intra and inter-segment coherence of the template, and the optimization method produces coherent generated melodies
Methodological contributions by means of machine learning methods for automatic music generation and classification
189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere
Learning and Recalling Melodies: A Computational Investigation Using the Melodic Recall Paradigm
Using melodic recall paradigm data, we describe an algorithmic approach to assessing melodic learning across multiple attempts. In a first simulation experiment, we reason for using similarity measures to assess melodic recall performance over previously utilized accuracy-based measures. In Experiment 2, with up to six attempts per melody, 31 participants sang back 28 melodies (length 15β48 notes) presented either as a piano sound or a vocal audio excerpt from real pop songs. Our analysis aimed to predict the similarity between the target melody and participantsβ sung recalls across successive attempts. Similarity was measured with different algorithmic measures reflecting various structural (e.g., tonality, intervallic) aspects of melodies and overall similarity. However, previous melodic recall research mentioned, but did not model, that the length of the sung recalls tends to increase across attempts, alongside overall performance. Consequently, we modeled how the attempt length changes alongside similarity to meet this omission in the literature. In a mediation analysis, we find that a target melodyβs length, but not other melodic features, is the main predictor of similarity via the attempt length. We conclude that sheer length constraints appear to be the main factor when learning melodies long enough to require several attempts to recall. Analytical features of melodic structure may be more important for shorter melodies, or with stimulus sets that are structurally more diverse than those found in the sample of pop songs used in this study
Methodological contributions by means of machine learning methods for automatic music generation and classification
189 p.Ikerketa lan honetan bi gai nagusi landu dira: musikaren sorkuntza automatikoa eta sailkapena. Musikaren sorkuntzarako bertso doinuen corpus bat hartu da abiapuntu moduan doinu ulergarri berriak sortzeko gai den metodo bat sortzeko. Doinuei ulergarritasuna hauen barnean dauden errepikapen egiturek ematen dietela suposatu da, eta metodoaren hiru bertsio nagusi aurkeztu dira, bakoitzean errepikapen horien definizio ezberdin bat erabiliz.Musikaren sailkapen automatikoan hiru ataza garatu dira: generoen sailkapena, familia melodikoen taldekatzea eta konposatzaileen identifikazioa. Musikaren errepresentazio ezberdinak erabili dira ataza bakoitzerako, eta ikasketa automatikoko hainbat teknika ere probatu dira, emaitzarik hoberenak zeinek ematen dituen aztertzeko.Gainbegiratutako sailkapenaren alorrean ere binakako sailkapenaren gainean lana egin da, aurretik existitzen zen metodo bat optimizatuz. Hainbat datu baseren gainean probatu da garatutako teknika, baita konposatzaile klasikoen piezen ezaugarriez osatutako datu base batean ere
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Towards an Intelligent Learning Environment for Melody Composition Through Formalisation of Narmour's <i>Implication-Realisation Model</i>
This thesis describes research with the ultimate goal o f finding ways to use artificial intelligence (AI) to encourage and facilitate melody composition by musical novices. The work described in this thesis forms the groundwork for the development of intelligent learning environments for novice composers in this domain. There were two stages to the research. The first stage was the formalisation and testing of an
existing analytical theory o f melody. This theory offers an explanation of how musical listeners break-up a melody into "chunks", and hear some notes as more important than others. The theory enables analysis of melodies in a hierarchical fashion. The formalisation process involved the implementation of a parser to create hierarchical analyses, and comparison of published analyses based on the theory with those created by the parser from the same melodies. From these results a critical evaluation of the analytical theory, and the parser, is
presented. The second stage o f the research involved extending the parser with constraint-based generation techniques. One result is an AI tool (called MOTIVE), which can generate melodies given an analysis (from the parser, or constructed by the user) and a set of constraints to be applied at each hierarchical level. The features of the tool are presented, and a general architecture for an intelligent learning environment is proposed, within which MOTIVE would reside, which shows how the formalised analytical theory from the first part of the work could be used educationally by novice composers of melody
Musical practices in the Balkans : Ethnomusicological perspectives
ΠΠ²Π° ΠΊΡΠΈΠ³Π° ΡΠ°Π΄ΡΠΆΠΈ ΡΡΡΠ΄ΠΈΡΠ΅ Π½Π°ΡΡΠ°Π»Π΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΈΠ·Π»Π°Π³Π°ΡΠ° Π½Π° ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΌ ΡΠΊΡΠΏΡ ΠΡΠ·ΠΈΡΠΊΠ΅ ΠΏΡΠ°ΠΊΡΠ΅ ΠΠ°Π»ΠΊΠ°Π½Π°: Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠ΅ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅, ΠΎΠ΄ΡΠΆΠ°Π½ΠΎΠΌ Π½ΠΎΠ²Π΅ΠΌΠ±ΡΠ° 2011. Π³ΠΎΠ΄ΠΈΠ½Π΅ Ρ ΠΠ΅ΠΎΠ³ΡΠ°Π΄Ρ. ΠΡΠ³Π°Π½ΠΈΠ·ΠΎΠ²Π°ΡΠ΅ΠΌ ΡΠΊΡΠΏΠ° ΠΆΠ΅Π»Π΅Π»ΠΈ ΡΠΌΠΎ, ΠΏΡΠ΅ ΡΠ²Π΅Π³Π°, Π΄Π° ΠΊΠ°ΠΎ Π΄ΠΎΠΌΠ°ΡΠΈΠ½ΠΈ ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠ³ ΡΠΊΡΠΏΠ° Π΄ΠΎΠΏΡΠΈΠ½Π΅ΡΠ΅ΠΌΠΎ ΡΡΠ²ΡΡΡΠΈΠ²Π°ΡΡ ΠΏΠΎΡΡΠΎΡΠ΅ΡΠΈΡ
ΠΊΠΎΠ½ΡΠ°ΠΊΠ°ΡΠ° ΠΌΠ΅ΡΡ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΈΠΌΠ° ΠΊΠΎΡΠΈ ΡΠ΅ Π±Π°Π²Π΅ ΠΌΡΠ·ΠΈΠΊΠ°ΠΌΠ° ΠΠ°Π»ΠΊΠ°Π½Π°, ΠΊΠ°ΠΎ ΠΈ Π΄Π° ΠΏΡΡΠΆΠΈΠΌΠΎ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π·Π° Π½ΠΎΠ²Π° ΠΏΡΠΎΡΠ΅ΡΠΈΠΎΠ½Π°Π»Π½Π° ΠΏΠΎΠ·Π½Π°Π½ΡΡΠ²Π°.
Π£ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡΠΊΠΎΠΌ ΡΠΌΠΈΡΠ»Ρ, Π½Π°ΠΌΠ΅ΡΠ° Π½Π°ΠΌ ΡΠ΅ Π±ΠΈΠ»Π° Π΄Π° ΡΡΠ²ΠΎΡΠΈΠΌΠΎ ΠΏΡΠΈΠ»ΠΈΠΊΡ Π·Π° ΠΏΡΠ΅ΠΈΡΠΏΠΈΡΠΈΠ²Π°ΡΠ΅ ΠΈ ΡΠ½Π°ΠΏΡΠ΅ΡΠΈΠ²Π°ΡΠ΅ ΡΠΎΠΏΡΡΠ²Π΅Π½ΠΈΡ
ΡΡΠ΅ΡΡΠ° Ρ Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠΈ, Π΄Π° Π΄ΠΈΡΠΊΡΡΠΎΠ²Π°ΡΠ΅ΠΌ ΠΎ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΠΌ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΊΠΈΠΌ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ°ΠΌΠ° ΠΏΡΠΈΠΌΠ΅ΡΠΈΠ²Π°Π½ΠΈΠΌ Π½Π° Π±Π°Π»ΠΊΠ°Π½ΡΠΊΠ΅ ΠΌΡΠ·ΠΈΡΠΊΠ΅ ΠΏΡΠ°ΠΊΡΠ΅ Ρ Π½ΠΎΠ²ΠΈΡΠ΅ Π²ΡΠ΅ΠΌΠ΅ Π΄ΠΎΠΏΡΠΈΠ½Π΅ΡΠ΅ΠΌΠΎ ΡΠ½Π°ΠΏΡΠ΅ΡΠ΅ΡΡ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ°, ΠΊΠ°ΠΎ ΠΈ Π΄Π° ΡΠΊΡΠ΅ΡΠ°ΡΠ΅ΠΌ ΠΏΠ°ΠΆΡΠ΅ Π½Π° ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½Π΅ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ΅, Π°Π»ΠΈ ΠΈ Π²ΡΠ΅Π΄Π½ΠΎΡΡΠΈ Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ βΠ½Π°ΡΠΈΠΎΠ½Π°Π»Π½ΠΈΡ
Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ°β
Π½Π° ΠΠ°Π»ΠΊΠ°Π½Ρ ΡΡΠ΅ΡΡΠ²ΡΡΠ΅ΠΌΠΎ Ρ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΈΡΠ΅ΠΌ ΡΠΊΡΡΡΠΈΠ²Π°ΡΡ βΠΌΠ°Π»ΠΈΡ
β Π΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΡΠ½ΠΈΡ
Π·Π°ΡΠ΅Π΄Π½ΠΈΡΠ° Ρ ΡΠ²Π΅ΡΡΠΊΠΎ Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΎ Π΄ΡΡΡΡΠ²ΠΎ. ΠΠ²Π°ΠΊΠ²Π΅, Ρ ΠΈΠ·Π²Π΅ΡΠ½ΠΎΠΌ ΡΠΌΠΈΡΠ»Ρ ΠΎΠΏΡΡΠ΅ ΠΏΠΎΡΡΠ΅Π±Π΅, ΠΏΠΎΡΠ΅Π½ΡΠΈΡΠ°Π½Π΅ ΡΡ ΠΎΠΊΠΎΠ»Π½ΠΎΡΡΠΈΠΌΠ° ΡΠ°Π·Π²ΠΎΡΠ° Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ Ρ Π‘ΡΠ±ΠΈΡΠΈ, ΠΏΠΎΡΠ΅Π±Π½ΠΎ ΠΏΠΎΡΠ»Π΅Π΄ΡΠΈΡ
Π΄Π΅ΡΠ΅Π½ΠΈΡΠ°.
ΠΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°ΡΠ° Π·Π° ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½Ρ Π½Π°ΡΠΎΠ΄Π½Ρ ΠΌΡΠ·ΠΈΠΊΡ ΠΈΠ·ΡΠ°ΠΆΠ΅Π½Π° ΠΊΡΠΎΠ· Π·Π°ΠΏΠΈΡΠ΅ ΠΎ ΡΠΎΡ, ΡΠ΅Π½ΠΎ Π½ΠΎΡΠΈΡΠ°ΡΠ΅ ΠΈ ΡΠ½ΠΈΠΌΠ°ΡΠ΅, Π° ΠΏΠΎΡΠΎΠΌ ΠΈ ΠΏΡΠ²Π΅ ΡΡΡΠ΄ΠΈΡΠ΅ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ
ΠΌΡΠ·ΠΈΡΠ°ΡΠ°, ΠΌΠ°Ρ
ΠΎΠΌ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ°, Ρ Π‘ΡΠ±ΠΈΡΠΈ ΡΡ (ΡΠ΅ΠΊ) 1948. Π΄ΠΎΠ±ΠΈΠ»ΠΈ ΠΈΠ½ΡΡΠΈΡΡΡΠΈΠΎΠ½Π°Π»Π½ΠΎ ΠΎΠΊΡΠΈΡΠ΅ ΠΎΡΠ½ΠΈΠ²Π°ΡΠ΅ΠΌ ΠΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΎΠ³ ΠΈΠ½ΡΡΠΈΡΡΡΠ° Π‘ΡΠΏΡΠΊΠ΅ Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ΅ Π½Π°ΡΠΊΠ° (Π΄Π°Π½Π°Ρ Π‘ΡΠΏΡΠΊΠ΅ Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ΅ Π½Π°ΡΠΊΠ° ΠΈ ΡΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ β Π‘ΠΠΠ£). ΠΠ΄ ΠΎΡΠ½ΠΈΠ²Π°ΡΠ° Π΄ΠΎ Π΄Π°Π½Π°Ρ ΡΠΎ ΡΠ΅ Π΄ΡΠΆΠ°Π²Π½Π° ΡΡΡΠ°Π½ΠΎΠ²Π°, ΡΠ΅ ΡΠ΅ Π½Π°ΡΠΈΠ½ ΡΠ°Π΄Π° ΡΡΠ»ΠΎΠ²ΡΠ΅Π½ Π΄ΡΠΆΠ°Π²Π½ΠΈΠΌ ΠΊΡΠ»ΡΡΡΠ½ΠΈΠΌ ΠΈ Π½Π°ΡΡΠ½ΠΈΠΌ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠ°ΠΌΠ°, Π° ΠΎΠ±ΠΈΠΌ Π½Π°ΡΡΠ½Π΅ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΡΠ΅ ΠΊΡΡΡΠ½ΠΎ ΡΠ΅ ΠΎΠ΄ΡΠ΅ΡΠΈΠ²Π°ΠΎ ΡΠ²Π΅ΠΊ ΠΌΠ°Π»ΠΈ Π±ΡΠΎΡ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ°. ΠΠΎΠ²Π΅ΡΠ°ΡΠ΅ Π±ΡΠΎΡΠ° Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³Π° Π·Π°ΠΏΠΎΡΠ»Π΅Π½ΠΈΡ
Ρ ΠΠ½ΡΡΠΈΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΠΈΡΠ°Π»ΠΎ ΡΠ΅ ΠΈΠ½ΠΈΡΠΈΡΠ°ΡΠΈΠ²ΠΎΠΌ Π΄Π° ΡΠ΅ Ρ ΠΎΠΊΠ²ΠΈΡΡ ΠΎΠ±Π΅Π»Π΅ΠΆΠ°Π²Π°ΡΠ° 170 Π³ΠΎΠ΄ΠΈΠ½Π° ΠΏΠΎΡΡΠΎΡΠ°ΡΠ° Π‘ΠΠΠ£, Π½Π°ΠΊΠΎΠ½ Π²Π΅ΡΠ΅Π³ Π±ΡΠΎΡΠ° ΡΠΊΡΠΏΠΎΠ²Π° ΠΊΠΎΡΠ΅ ΡΡ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΎΠ²Π°Π»ΠΈ ΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ·ΠΈ ΠΈΠ· ΠΠ½ΡΡΠΈΡΡΡΠ°, ΠΏΡΠΈΡΠ΅Π΄ΠΈ ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½ΠΈ Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΈ ΡΠΊΡΠΏ. Π’ΠΎ ΡΠ΅, ΡΡΠ΅Π΄Π½ΠΎ, Π±ΠΈΠΎ ΠΈ ΠΏΡΠ²ΠΈ ΡΠΏΠ΅ΡΠΈΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½ΠΈ Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΈ ΡΠΈΠΌΠΏΠΎΠ·ΠΈΡΡΠΌ Ρ Π‘ΡΠ±ΠΈΡΠΈ, Π° ΡΡ
ΠΎΠ΄Π½ΠΎ ΡΠΎΠΌΠ΅, ΠΎΠ²ΠΎ ΡΠ΅ ΠΏΡΠ²ΠΈ Π·Π±ΠΎΡΠ½ΠΈΠΊ ΡΠ°ΠΊΠ²ΠΎΠ³ ΠΏΡΠΎΡΠΈΠ»Π° ΠΎΠ±ΡΠ°Π²ΡΠ΅Π½ Ρ Π½Π°ΡΠΎΡ Π·Π΅ΠΌΡΠΈ. ΠΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½Π° ΠΏΠΎΠ΄ΡΡΠΊΠ° ΠΊΠΎΡΡ ΡΠΌΠΎ Π΄ΠΎΠ±ΠΈΠ»ΠΈ ΠΎΠ΄ Π‘ΠΠΠ£ β ΠΠ΄Π΅ΡΠ΅ΡΠ° Π»ΠΈΠΊΠΎΠ²Π½Π΅ ΠΈ ΠΌΡΠ·ΠΈΡΠΊΠ΅ ΡΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ, ΠΊΠ°ΠΎ ΠΈ ΠΏΡΠΈΠ·Π½Π°Π²Π°ΡΠ΅ Π²Π°ΠΆΠ½ΠΎΡΡΠΈ ΠΎΠ²Π°ΠΊΠ²ΠΎΠ³ ΡΠΊΡΠΏΠ° ΠΎΠ΄ ΡΡΡΠ°Π½Π΅ ΠΠΈΠ½ΠΈΡΡΠ°ΡΡΡΠ²Π° ΠΏΡΠΎΡΠ²Π΅ΡΠ΅ ΠΈ Π½Π°ΡΠΊΠ΅ Π Π΅ΠΏΡΠ±Π»ΠΈΠΊΠ΅ Π‘ΡΠ±ΠΈΡΠ΅, ΡΡΠΎ ΡΠ΅ ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π°Π»ΠΎ ΡΠΈΠ½Π°Π½ΡΠΈΡΡΠΊΡ ΠΏΠΎΠΌΠΎΡ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠΈ, Π±ΠΈΠ»ΠΈ ΡΡ Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ»ΠΎΠ²ΠΈ ΠΈ Π·Π° ΡΠ°ΠΌΡ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΡΡ ΡΠΊΡΠΏΠ°, ΠΈ Π·Π° ΠΎΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΎΠ²ΠΎΠ³ Π·Π±ΠΎΡΠ½ΠΈΠΊΠ°. ΠΠ°ΠΎ ΠΈ ΡΠ²Π΅ΠΊ, ΠΎΠ±ΠΈΠΌ ΠΏΠΎΠΌΠΎΡΠΈ, Π° ΠΏΠΎΡΠ΅Π±Π½ΠΎ ΡΠΎΠΊΠΎΠ²ΠΈ, Π±ΠΈΠ»ΠΈ ΡΡ Π»ΠΈΠΌΠΈΡΠΈΡΠ°ΡΡΡΠΈ ΡΠ°ΠΊΡΠΎΡΠΈ Π½Π°ΡΠΈΠΌ ΠΈΠ΄Π΅ΡΠ°ΠΌΠ° ΠΈ ΠΏΡΠΎΡΠ΅ΡΠΈΠΎΠ½Π°Π»Π½ΠΈΠΌ ΠΆΠ΅ΡΠ°ΠΌΠ°. ΠΠ·ΡΠ°ΠΆΠ°Π²Π°ΠΌΠΎ Π½Π°ΡΠΎΡΠΈΡΡ Π·Π°Ρ
Π²Π°Π»Π½ΠΎΡΡ ΡΠ²Π°ΠΆΠ΅Π½ΠΈΠΌ ΠΊΠΎΠ»Π΅Π³Π°ΠΌΠ° ΠΊΠΎΡΠΈ ΡΡ Π±ΠΈΠ»ΠΈ Ρ ΡΠ°ΡΡΠ°Π²Ρ ΠΡΠΎΠ³ΡΠ°ΠΌΡΠΊΠΎΠ³ ΠΎΠ΄Π±ΠΎΡΠ° ΡΠΊΡΠΏΠ°: ΠΠ·Π°ΡΠΈΡΡ ΠΠ΅ΠΌΡΠΎΠ²ΡΠΊΠΎΠΌ, ΠΠΈΠΌΡ Π‘Π°ΠΌΡΠΎΠ½Ρ, ΠΠ°ΡΡΠΈΠ½Ρ Π‘ΡΠΎΡΠΊΡΡ, ΠΠΎΠ·Π°Π½ΠΊΠΈ ΠΠ΅ΡΡΠ΅Π²ΠΎΡ ΠΈ ΠΠ°Π»Ρ Π ΠΈΡ
ΡΠ΅ΡΡ. ΠΠ΅ΠΏΠΎΡΡΠ΅Π΄Π½Π΅ Π·Π°Π΄Π°ΡΠΊΠ΅ ΠΈΠ· Π΄ΠΎΠΌΠ΅Π½Π° ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡΠ΅ ΡΠΊΡΠΏΠ° Π΄Π΅Π»ΠΈΠ»ΠΈ ΡΠΌΠΎ ΠΏΡΠ²Π΅Π½ΡΡΠ²Π΅Π½ΠΎ ΡΠ° ΠΊΠΎΠ»Π΅Π³Π°ΠΌΠ°-Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ·ΠΈΠΌΠ° ΠΈΠ· ΠΠ½ΡΡΠΈΡΡΡΠ°, Π Π°ΡΡΠΊΠΎΠΌ ΠΠ°ΠΊΠΎΠ²ΡΠ΅Π²ΠΈΡΠ΅ΠΌ ΠΈ ΠΠ°ΡΠΈΡΠΎΠΌ ΠΡΠΌΠ½ΠΈΡ. ΠΠ΅Π»ΠΈΠΊΠΎ Π½Π°ΠΌ ΡΠ΅ Π·Π°Π΄ΠΎΠ²ΠΎΡΡΡΠ²ΠΎ ΡΡΠΎ ΡΠΌΠΎ ΠΈΠΌΠ°Π»ΠΈ ΠΏΡΠΈΠ»ΠΈΠΊΡ Π΄Π° ΠΎΠΊΡΠΏΠΈΠΌΠΎ 26 Π΅ΡΠ½ΠΎΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³Π° ΠΈΠ· 11 Π·Π΅ΠΌΠ°ΡΠ°: ΠΡΠ³Π°ΡΡΠΊΠ΅, ΠΠ΅Π»ΠΈΠΊΠ΅ ΠΡΠΈΡΠ°Π½ΠΈΡΠ΅, ΠΡΡΠΊΠ΅, ΠΠΈΡΠ²Π°Π½ΠΈΡΠ΅, ΠΠ°ΡΠ°ΡΡΠΊΠ΅, ΠΠ°ΠΊΠ΅Π΄ΠΎΠ½ΠΈΡΠ΅, Π ΡΡΠΈΡΠ΅, Π‘Π»ΠΎΠ²Π΅Π½ΠΈΡΠ΅, Π’ΡΡΡΠΊΠ΅, ΠΡΡΡΡΠ°Π»ΠΈΡΠ΅ ΠΈ Π‘ΡΠ±ΠΈΡΠ΅. ΠΠ°ΠΎ Π½Π°ΠΌ ΡΠ΅ ΡΡΠΎ, Π·Π±ΠΎΠ³ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈΡ
ΠΎΠΊΠΎΠ»Π½ΠΎΡΡΠΈ, Π½Π° ΡΠΊΡΠΏΡ Π½ΠΈΡΡ ΠΌΠΎΠ³Π»ΠΈ Π΄Π° ΡΠ·ΠΌΡ ΡΡΠ΅ΡΡΠ° Π½Π΅ΠΊΠΈ ΠΎΠ΄ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ° ΠΌΡΠ·ΠΈΠΊΠ° ΠΠ°Π»ΠΊΠ°Π½Π° ΡΠ° Π΄ΡΡΠ³ΠΈΡ
ΠΏΠΎΠ΄ΡΡΡΡΠ°, ΡΠΈΡΠ° Π±ΠΈ ΠΈΡΠΊΡΡΡΠ²Π° Π½Π΅ΡΡΠΌΡΠΈΠ²ΠΎ Π΄ΠΎΠΏΡΠΈΠ½Π΅Π»Π° ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΡ ΡΠΈΠΌΠΏΠΎΠ·ΠΈΡΡΠΌΠ° ΠΈ Π·Π±ΠΎΡΠ½ΠΈΠΊΠ°. ΠΠ΅Π»ΠΈΠΊΠ° Π²Π΅ΡΠΈΠ½Π° ΡΡΠ΅ΡΠ½ΠΈΠΊΠ° ΡΠΊΡΠΏΠ° ΡΠ΅ ΠΏΡΠΈΡΠ΅Π΄ΠΈΠ»Π° ΡΠ²ΠΎΡΠ° ΠΈΠ·Π»Π°Π³Π°ΡΠ° Π·Π° ΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°ΡΠ΅.
Π‘Π°Π΄Π° ΡΠ΅ ΠΏΡΠ΅Π΄ ΡΠΈΡΠ°ΠΎΡΠΈΠΌΠ° Π½Π°Π»Π°Π·ΠΈ Π΄Π²Π°Π΄Π΅ΡΠ΅Ρ ΡΠ΅Π΄Π½Π° ΡΡΡΠ΄ΠΈΡΠ° ΠΊΠΎΡΠ° Π½Π° ΡΠ°Π·Π»ΠΈΡΠΈΡΠ΅ Π½Π°ΡΠΈΠ½Π΅ ΠΎΡΠ²Π΅ΡΡΠ°Π²Π° ΠΌΡΠ·ΠΈΡΠΊΠ΅ ΠΏΡΠ°ΠΊΡΠ΅ ΠΠ°Π»ΠΊΠ°Π½Π°, ΡΠ²Π΅Π΄ΠΎΡΠ΅ΡΠΈ ΠΎ ΡΠΈΡΠΈΠ½ΠΈ Π°ΠΊΡΡΠ΅Π»Π½ΠΈΡ
ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠΊΠΈΡ
ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°ΡΠ° ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡΠ°. ΠΠ·Π΄Π°ΡΠ΅ ΡΠΊΡΡΡΡΡΠ΅ ΠΈ Π²ΡΠ΅Π΄Π½Π΅ Π°ΡΠ΄ΠΈΠΎ ΠΈ Π²ΠΈΠ΄Π΅ΠΎ ΠΏΡΠΈΠΌΠ΅ΡΠ΅ Π½Π° ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΎΠΌ Π΄ΠΈΡΠΊΡ, ΡΠΈΠΌΠ΅ ΡΠΈΡΠ°ΠΎΡΠΈΠΌΠ° ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π°ΠΌΠΎ ΠΏΠΎΡΠΏΡΠ½ΠΈΡΠΈ ΡΠ²ΠΈΠ΄ Ρ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°Π½Ρ ΠΌΡΠ·ΠΈΠΊΡ ΠΈ Π½Π°ΡΡΠ½Π΅ ΠΏΡΠΈΡΡΡΠΏΠ΅. ΠΠ±ΡΠ°ΡΠ°ΡΡΡΠΈ ΡΠ΅ ΡΠΈΡΠ΅ΠΌ ΠΊΡΡΠ³Ρ ΡΠΈΡΠ°Π»Π°ΡΠ°, Π·Π±ΠΎΡΠ½ΠΈΠΊ ΡΠ΅ ΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½ Π½Π° Π΅Π½Π³Π»Π΅ΡΠΊΠΎΠΌ ΡΠ΅Π·ΠΈΠΊΡ, Π°Π»ΠΈ ΡΠ΅ Π°ΡΡΠΎΡΠΈΠΌΠ° ΠΎΡΡΠ°Π²ΡΠ΅Π½Π° ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π΄Π° ΡΠ²ΠΎΡΠ΅ ΡΠ°Π΄ΠΎΠ²Π΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅ ΠΈ Π½Π° ΠΌΠ°ΡΠ΅ΡΡΠΈΠΌ ΡΠ΅Π·ΠΈΡΠΈΠΌΠ°, ΠΏΠ° ΡΡ ΠΎΠ²Π΅ Π²Π΅ΡΠ·ΠΈΡΠ΅ ΡΠ°ΠΊΠΎΡΠ΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½Π΅ Π½Π° Π΄ΠΈΡΠΊΡ.
Π£ΡΠ΅ΡΠΈΠ²Π°ΡΠ΅ ΠΎΠ²ΠΎΠ³ ΠΎΠ±ΠΈΠΌΠ½ΠΎΠ³ ΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ Π·Π±ΠΎΡΠ½ΠΈΠΊΠ° ΡΠ΅ Π±ΠΈΠ»ΠΎ ΠΏΠΎΡΠ΅Π±Π½ΠΎ ΠΈΡΠΊΡΡΡΠ²ΠΎ. ΠΠ°Ρ
Π²Π°ΡΡΡΠ΅ΠΌΠΎ ΡΠ΅ ΡΠ²ΠΈΠΌ Π°ΡΡΠΎΡΠΈΠΌΠ° Π·Π° ΠΏΡΠΈΠ»ΠΎΠ³Π΅ ΠΈ ΡΠ°ΡΠ°Π΄ΡΡ. ΠΠΎΡΠ΅Π±Π½Ρ Π·Π°Ρ
Π²Π°Π»Π½ΠΎΡΡ Π΄ΡΠ³ΡΡΠ΅ΠΌΠΎ ΡΠ΅ΡΠ΅Π½Π·Π΅Π½ΡΠΈΠΌΠ° ΡΡΡΠ΄ΠΈΡΠ°. Π£ ΠΎΠ²ΠΎΠΌ ΠΎΠ±ΠΈΠΌΠ½ΠΎΠΌ ΠΏΠΎΡΠ»Ρ ΠΏΠΎΠ΄ΡΡΠΊΡ ΡΠΌΠΎ ΠΈΠΌΠ°Π»ΠΈ ΠΎΠ΄ ΡΠΈΡΠ°Π²ΠΎΠ³ ΠΊΠΎΠ»Π΅ΠΊΡΠΈΠ²Π° ΠΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΎΠ³ ΠΈΠ½ΡΡΠΈΡΡΡΠ°, Π° Π½Π°ΡΠΎΡΠΈΡΠΎ ΠΎΠ΄ ΠΊΠΎΠ»Π΅Π³ΠΈΠ½ΠΈΡΠ΅ Π΄Ρ ΠΠ΅Π»ΠΈΡΠ΅ ΠΠΈΠ»ΠΈΠ½, ΠΊΠΎΡΠ° ΡΠ΅ ΠΈΠΌΠ°Π»Π° ΠΌΠ½ΠΎΡΡΠ²ΠΎ Π΄ΡΠ°Π³ΠΎΡΠ΅Π½ΠΈΡ
ΡΡΠ³Π΅ΡΡΠΈΡΠ°. ΠΠ½Π³Π°ΠΆΠΎΠ²Π°ΡΠ΅ Π΄Ρ ΠΠ΅Π»Π΅Π½Π΅ Π‘ΠΈΠΌΠΎΠ½ΠΎΠ²ΠΈΡ-Schiff (ΠΡΠΆΠ°Π²Π½ΠΈ ΡΠ½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅Ρ ΠΠΎΡΡΠ»Π°Π½Π΄Π°, Π‘ΠΠ) ΠΎΠΊΠΎ Π»Π΅ΠΊΡΠΎΡΠΈΡΠ°ΡΠ° ΡΠ΅ΠΊΡΡΠΎΠ²Π° Π½Π° Π΅Π½Π³Π»Π΅ΡΠΊΠΎΠΌ ΡΠ΅Π·ΠΈΠΊΡ, ΠΠΎΡΠ°Π½Π° ΠΠ΅ΡΠΊΠΎΠ²ΠΈΡΠ° Ρ ΠΏΡΠΈΠΏΡΠ΅ΠΌΠΈ Π·Π²ΡΡΠ½ΠΈΡ
ΠΏΡΠΈΠΌΠ΅ΡΠ°, ΠΠΈΠ»ΠΎΡΠ° Π Π°ΡΠΈΡΠ° Ρ ΠΎΠ±ΡΠ°Π΄ΠΈ Π²ΠΈΠ΄Π΅ΠΎ- ΠΏΡΠΈΠΌΠ΅ΡΠ° ΠΈ ΠΠΎΡΠ°Π½Π° ΠΠ°ΡΠΈΡΠ° Π½Π° ΡΠ΅Ρ
Π½ΠΈΡΠΊΠΎΡ ΠΏΡΠΈΠΏΡΠ΅ΠΌΠΈ ΠΈΠ·Π΄Π°ΡΠ°, Π΄Π°Π»Π΅ΠΊΠΎ ΡΠ΅ ΠΏΡΠ΅Π²Π°Π·ΠΈΡΠ»ΠΎ Π±Π°Π·ΠΈΡΠ½Π΅ Π·Π°Π΄Π°ΡΠΊΠ΅, Π·Π±ΠΎΠ³ ΡΠ΅Π³Π° ΡΠΌΠΎ ΠΈΠΌ Π²Π΅Π»ΠΈΠΊΠΈ Π΄ΡΠΆΠ½ΠΈΡΠΈ.This book is comprised of studies presented at the international symposium Musical practices in the Balkans: ethnomusicological perspectives, held in November 2011 in Belgrade, Serbia. By organizing this international meeting, as hosts, we above all wanted to contribute to strengthening the existing ties among researchers involved with Balkan musics and to facilitate new professional
contacts. Our intent was to create an opportunity for reassessment and improvement of each of our own shares in ethnomusicology, to contribute to the advancement of research by discussing various research strategies recently applied to Balkan musical practices, and to participate in the more efficient inclusion of βsmallβ disciplinary communities in the global ethnomusicologies by highlighting specific values and problems of βnational ethnomusicologiesβ in the Balkans. These, so to speak, general needs, were reinforced in circumstances of the growth of the ethnomusicology in Serbia, particularly in recent decades.
The interests in traditional folk music expressed through writings, notation, and recording of the same, followed by the first research studies by educated musiciansβmainly composers, became institutionalized in Serbia (only) in 1948 with foundation of the Institute of musicology of Serbian academy of sciences (today Serbian academy of sciences and artsβSASA). Since its beginning, SASA has been a state institution, with its work regimen determined by the government culture and science politics and the scope of research output administered by a small number of researchers. The increase in number of ethnomusicologists employed by the Institute resulted in an initiative to celebrate the SASA 170th anniversary by organizing an ethnomusicology conference, in the footsteps of a number of similar professional meetings coordinated by the Instituteβs musicologists. This was at the same time the first narrowly specialized ethnomusicology symposium in Serbia, and consequently, in front of you is the first proceedings of such profile published in our country. The substantial support received from the SASA Department of fine arts and music and the acknowledgement of the significance of such a meeting by the Ministry of education and science of the Republic of Serbia, including funding the event organization, were essential and indispensable prerequisites for the Symposium realization and publication of this book. As is usually the case, the extent of financial support and particularly deadlines, imposed limits on some of our ideas and professional desires. We are deeply grateful to our esteemed colleagues, members of the Symposium Program committee: Izaly Zemtsovsky, Jim Samson, Martin Stokes, Lozanka Peycheva, and PΓ‘l Richter. The Symposium immediate logistics duties were helpfully shared primarily with our colleagues, the Institute ethnomusicologists, Rastko JakovljeviΔ and Marija DumniΔ.
It was our satisfaction to have the opportunity to gather twenty-six ethnomusicologists from eleven countries: Bulgaria, Great Britain, Greece, Hungary, Lithuania, Macedonia, Russia, Slovenia, Turkey, Australia, and Serbia.
We regret that due to objective circumstances, some of the Balkans researchers from other regions, whose experiences would unquestionably contribute to the quality of the Symposium and these proceedings, could not take part. The vast majority of the Symposium participants prepared their papers for publication.
The readers are offered twenty-one studies that in different ways illuminate Balkan musical practices and testify to the breadth of current research interests and methodologies. The publication also includes a compact disc with useful audio and video examples, assembled with the idea to provide the reader with even more complete insight into the researched music and utilized approaches. In order to address a wider audience, the proceedings are published in English.
Since the contributing authors could optionally submit an additional version of the paper in their native language, those versions are also provided on the compact disc.
Editing of this extensive and comprehensive publication presented a unique experience. We would like to thank all of the authors for submitting their papers and for their cooperation. We also extend our sincere gratitude to our reviewers. In this voluminous undertaking, we enjoyed the support of our colleagues at the Institute, but in particular, the encouragement from Melita Milin who offered a number of valuable suggestions. We truly appreciate the cooperation of Jelena SimonoviΔ Schiff (Portland State University, USA) in proof reading papers in English, Zoran JerkoviΔ in mastering of audio examples, MiloΕ‘ RaΕ‘iΔ in preparing of video examples, and Goran JanjiΔ in technical preparation for print that exceeded standard duties and helped shape this publication.ΠΠ±ΡΠ°Π²ΡΠΈΠ²Π°ΡΠ΅ ΠΎΠ²ΠΎΠ³ Π·Π±ΠΎΡΠ½ΠΈΠΊΠ° ΡΠΈΠ½Π°Π½ΡΠΈΡΡΠΊΠΈ ΡΠ΅ ΠΏΠΎΠΌΠΎΠ³Π»ΠΎ ΠΠΈΠ½ΠΈΡΡΠ°ΡΡΡΠ²ΠΎ ΠΏΡΠΎΡΠ²Π΅ΡΠ΅, Π½Π°ΡΠΊΠ΅ ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΡΠΊΠΎΠ³ ΡΠ°Π·Π²ΠΎΡΠ° Π Π΅ΠΏΡΠ±Π»ΠΈΠΊΠ΅ Π‘ΡΠ±ΠΈΡΠ΅. / These proceedings have been financially supported by Ministry of education, science and technological development of the Republic of Serbia.
ΠΠ±ΠΎΡΠ½ΠΈΠΊ ΡΠ°Π΄ΠΎΠ²Π° ΡΠ° Π½Π°ΡΡΠ½ΠΎΠ³ ΡΠΊΡΠΏΠ° ΠΎΠ΄ΡΠΆΠ°Π½ΠΎΠ³ ΠΎΠ΄ 23. Π΄ΠΎ 25. Π½ΠΎΠ²Π΅ΠΌΠ±ΡΠ° 2011. / Proceeding of the international conference held from november 23 to 25. , 2011
Hearing in the mind\u27s ear: A PET investigation of musical imagery and perception
Neuropsychological studies have suggested that imagery processes may be mediated by neuronal mechanisms similar to those used in perception. To test this hypothesis, and to explore the neural basis for song imagery, 12 normal subjects were scanned using the water bolus method to measure cerebral blood flow (CBF) during the performance of three tasks. In the control condition subjects saw pairs of words on each trial and judged which word was longer. In the perceptual condition subjects also viewed pairs of words, this time drawn from a familiar song; simultaneously they heard the corresponding song, and their task was to judge the change in pitch of the two cued words within the song. In the imagery condition, subjects performed precisely the same judgment as in the perceptual condition, but with no auditory input. Thus, to perform the imagery task correctly an internal auditory representation must be accessed. Paired-image subtraction of the resulting pattern of CBF, together with matched MRI for anatomical localization, revealed that both perceptual and imagery. tasks produced similar patterns of CBF changes, as compared to the control condition, in keeping with the hypothesis. More specifically, both perceiving and imagining songs are associated with bilateral neuronal activity in the secondary auditory cortices, suggesting that processes within these regions underlie the phenomenological impression of imagined sounds. Other CBF foci elicited in both tasks include areas in the left and right frontal lobes and in the left parietal lobe, as well as the supplementary motor area. This latter region implicates covert vocalization as one component of musical imagery. Direct comparison of imagery and perceptual tasks revealed CBF increases in the inferior frontal polar cortex and right thalamus. We speculate that this network of regions may be specifically associated with retrieval and/or generation of auditory information from memory
Acquired and congenital disorders of sung performance: A review.
Many believe that the majority of people are unable to carry a tune. Yet, this
widespread idea underestimates the singing abilities of the layman. Most
occasional singers can sing in tune and in time, provided that they perform at a
slow tempo. Here we characterize proficient singing in the general population
and identify its neuronal underpinnings by reviewing behavioral and neuroimaging
studies. In addition, poor singing resulting from a brain injury or neurogenetic
disorder (i.e., tone deafness or congenital amusia) is examined. Different lines
of evidence converge in indicating that poor singing is not a monolithic
deficit. A variety of poor-singing "phenotypes" are described,
with or without concurrent perceptual deficits. In addition, particular
attention is paid to the dissociations between specific abilities in poor
singers (e.g., production of absolute vs. relative pitch, pitch vs. time
accuracy). Such diversity of impairments in poor singers can be traced to
different faulty mechanisms within the vocal sensorimotor loop, such as pitch
perception and sensorimotor integration
Neural processing of poems and songs is based on melodic properties
The neural processing of speech and music is still a matter of debate. A long tradition that assumes shared processing capacities for the two domains contrasts with views that assume domain-specific processing. We here contribute to this topic by investigating, in a functional magnetic imaging (fMRI) study, ecologically valid stimuli that are identical in wording and differ only in that one group is typically spoken (or silently read), whereas the other is sung: poems and their respective musical settings. We focus on the melodic properties of spoken poems and their sung musical counterparts by looking at proportions of significant autocorrelations (PSA) based on pitch values extracted from their recordings. Following earlier studies, we assumed a bias of poem-processing towards the left and a bias for song-processing on the right hemisphere. Furthermore, PSA values of poems and songs were expected to explain variance in left- vs. right-temporal brain areas, while continuous liking ratings obtained in the scanner should modulate activity in the reward network. Overall, poem processing compared to song processing relied on left temporal regions, including the superior temporal gyrus, whereas song processing compared to poem processing recruited more right temporal areas, including Heschl's gyrus and the superior temporal gyrus. PSA values co-varied with activation in bilateral temporal regions for poems, and in right-dominant fronto-temporal regions for songs. Continuous liking ratings were correlated with activity in the default mode network for both poems and songs. The pattern of results suggests that the neural processing of poems and their musical settings is based on their melodic properties, supported by bilateral temporal auditory areas and an additional right fronto-temporal network known to be implicated in the processing of melodies in songs. These findings take a middle ground in providing evidence for specific processing circuits for speech and music in the left and right hemisphere, but simultaneously for shared processing of melodic aspects of both poems and their musical settings in the right temporal cortex. Thus, we demonstrate the neurobiological plausibility of assuming the importance of melodic properties in spoken and sung aesthetic language alike, along with the involvement of the default mode network in the aesthetic appreciation of these properties
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