Suchergebnisse

Prolog -- Erster Teil Die Geige -- I. Kapitel Ursprung der Geige -- II. Kapitel Tradition und der Gelehrte (Ein Zwischenspiel) -- III. Kapitel Eine Familienähnlichkeit -- IV. Kapitel Die alten Völker -- V. Kapitel Ein Wandern -- VI. Kapitel Die Musik in den ersten Jahrhunderten -- VII. Kapitel Erste Bogeninstrumente in Europa -- VIII. Kapitel Eine Begegnung -- IX. Kapitel Der Minstrel und Musikant im romantischen Zeitalter -- X. Kapitel Ein Rückblick -- XI. Kapitel Rivalen -- XII. Kapitel Das Instrument der Ehrbarkeit -- XIII. Kapitel Die Violine -- XIV. Kapitel Zwei Gasparo -- XV. Kapitel Maggini und andere Brescianer Meister -- XVI. Kapitel Die Amatis -- XVII. Kapitel Aus der Vogelschau -- XVIII. Kapitel Die Schule der Amati -- XIX. Kapitel Die Familie der Guarneri -- XX. Kapitel Jacobus Stainer -- XXI. Kapitel Der Grösste von allen -- XXII. Kapitel Giuseppe Guarneri del Gesu -- XXIII. Kapitel Der Geigenbau in Frankreich, England und Deutschland -- XXIV. Kapitel Ist's ein Geheimnis? -- Zweiter Teil Geigenspiel und Geigenspieler -- I. Kapitel Präludium -- II. Kapitel Die Geigenkunst in Italien -- III. Kapitel Die Geigenkunst in Italien (Fortsetzung) -- IV. Kapitel Viotti -- V. Kapitel Der Virtuose der alten Zeit -- VI. Kapitel Paganini: Eine Studie -- VII. Kapitel Das Violinspiel in Deutschland -- VIII. Kapitel Die Violinkunst in Deutschland (Fortsetzung) -- IX. Kapitel Die Violinkunst in Frankreich -- X. Kapitel Die Violinkunst in Frankreich (Fortsetzung) -- XI. Kapitel Die Violinkunst in England -- XII. Kapitel Die Geigerin (Eine Rhapsodie) -- Dritter Teil Ein Umriss der Entwicklung der Violinkomposition -- I. Kapitel In ihrer Kindheit -- II. Kapitel Die Herrschaft der Sonate -- III. Kapitel Die Sonata da Chiesa -- IV. Kapitel Die Herrschaft des Violinkonzerts -- V. Kapitel Eine neue Ära des Violinkonzerts -- VI. Kapitel Letzte Ära des Violinkonzerts -- VII. Kapitel Didaktische Violinliteratur -- VIII. Kapitel Ein Ausbund -- AnhÄnge -- Anhang A -- Bemerkungen üiber den dem frühen Violinengeschlecht beigelegten Namen Fiedel -- Martin Agricola -- Michael Praetorius und Ganassi del Fontego -- Zur Entwickelung des Bogens -- Teile einer Violine -- Anhang B Liste der Geigenbauer -- Geigenbauer der Brescianer Schule -- Schüler und Nachahmer der Amatis -- Schüler und Nachahmer Stradivaris -- Verschiedene andere italienische Meister -- Französische, englische und deutsche Geigenbauer -- Anhang C -- Violinistischer Stammbaum von Meistern und Schülern seit der Gründung der römischen Schule und einige unabhängige Gruppen von Geigern -- Register.

From the songs of Arab diva Umm Khultum on the banks of the Tigris to the strains of a young boy playing the violin for his mother in Melbourne, to the swing jazz of the nightclubs and cabarets of 1940s Baghdad, a fisherman playing a flute on the banks of the Mekong, and Paganini in the borderlands of eastern Poland.

Comunicació presentada a: 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, que va tenir lloc el 13 de novembre de 2017 a Glasgow, UK. ; Though musicians tend to agree on the importance of practicing expressivity in performance, not many tools and techniques are available for the task. A machine learning model is proposed for predicting bowing velocity during performances of violin pieces. Our aim is to provide feedback to violin students in a technology–enhanced learning setting. Predictions are generated for musical phrases in a score by matching them to melodically and rhythmically similar phrases in performances by experts and adapting the bow velocity curve measured in the experts' performance. Results show that mean error in velocity predictions and bowing direction classification accuracy outperform our baseline when reference phrases similar to the predicted ones are available. ; This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).

Background: Expression is a key skill in music performance, and one that is difficult to address in music lessons. Computational models that learn from expert performances can help providing suggestions and feedback to students. Aim: We propose and analyze an approach to modeling variations in dynamics and note onset timing for solo violin pieces with the purpose of facilitating expressive performance learning in new pieces, for which no reference performance is available. Method: The method generates phrase–level predictions based on musical score information on the assumption that expressiveness is idiomatic, and thus influenced by similar–sounding melodies. Predictions were evaluated numerically using three different datasets and against note–level machine–learning models, and also perceptually by listeners, who were presented to synthesized versions of musical excerpts, and asked to choose the most human–sounding one. Some of the presented excerpts were synthesized to reflect the variations in dynamics and timing predicted by the model, whereas others were shaped to reflect the dynamics and timing of an actual expert performance, and a third group was presented with no expressive variations. Results: surprisingly, none of the three synthesized versions was consistently selected as human–like nor preferred with statistical significance by listeners. Possible interpretations of these results include the fact that the melodies might have been impossible to interpret outside their musical context, or that expressive features that were left out of the modeling such as note articulation and vibrato are, in fact, essential to the perception of expression in violin performance. Positive feedback by some listeners toward the modeled melodies in a blind setting indicate that the modeling approach was capable of generating appropriate renditions at least for a subset of the data. Numerically, performance in phrase–level suffers a small degradation if compared to note–level, but produces predictions easier to interpret visually, thus more useful in a pedagogical setting. ; This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).

Comunicació presentada a: 10th International Workshop on Machine Learning and Music (MML), celebrat a Barcelona (Espanya), el 6 d'octubre de 2017. ; A model is proposed for predicting expressive variations in dynamics for violin performances with the purpose of facilitating expressive performance learning by students. The model uses phrases rather than single notes as units of analysis in a lazy learning approach: each phrase in a new score is matched to phrases from expressive performances by experts, adapting the experts' transformations to render an expressive performance of the new score. In preliminary tests, the model approximates the dynamics of actual performances better than an unexpressive baseline model whenever the reference dataset contains melodies similar to those being predicted. ; This work has been partly sponsored by the Spanish TIN project TIMUL (TIN 2013-48152-C2-2-R), the European Union Horizon 2020 research and innovation programme under grant agreement No. 688269 (TELMI project), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).