415 research outputs found
ΠΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ° / Musicology (34 I/2023)
ΠΠ±Π΅Π»Π΅ΠΆΠ°Π²Π°ΡΠ΅ ΠΏΠΎΠ»Π° Π²Π΅ΠΊΠ° ΠΎΠ΄ ΠΊΠ°Π΄Π° ΡΠ΅ ΠΏΡΠ΅ΠΌΠΈΠ½ΡΠΎ ΠΠ³ΠΎΡ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΈ (1882β1971) ΠΏΡΠΎΡΠ΅ΠΊΠ»ΠΎ ΡΠ΅ Ρ ΡΠ΅Π½ΡΠΈ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΡΠ΅ ΠΊΠΎΠ²ΠΈΠ΄Π°, ΠΏΠ° ΡΠ΅ Π½Π°ΡΡΠ½ΠΎ ΡΠ°Π·ΠΌΠ°ΡΡΠ°ΡΠ΅ ΠΏΠΎΠ΄ΡΡΠ°ΠΊΠ½ΡΡΠΎ ΡΠΎΠΌ Π³ΠΎΠ΄ΠΈΡΡΠΈΡΠΎΠΌ ΠΏΡΠΎΠ΄ΡΠΆΠΈΠ»ΠΎ. Π’Π°ΠΊΠΎ ΡΠ΅ ΡΠ΅ΠΌΡ ΠΏΡΠΈΠ΄ΡΡΠΆΡΡΠ΅ ΠΈ ΠΠ»Π°Π²Π½Π° ΡΠ΅ΠΌΠ° Ρ Π½ΠΎΠ²ΠΎΠΌ Π±ΡΠΎΡΡ ΠΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ (34), Ρ Π½ΠΈΠ·ΠΎΠΌ ΡΡΡΠ΄ΠΈΡΠ° ΠΏΠΎΡΠ²Π΅ΡΠ΅Π½ΠΈΡ
Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠΌ, ΠΊΠΎΡΠ΅ ΠΏΠΎΡΠΈΡΡ ΠΈΠ· ΠΈΠ·Π»Π°Π³Π°ΡΠ° Π½Π° Π‘ΡΡΠ΄ΠΈΡΡΠΊΠΎΠΌ Π΄Π°Π½Ρ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΎΠ²Π°Π½ΠΎΠΌ 2021. Π³ΠΎΠ΄ΠΈΠ½Π΅ Π½Π° ΠΠ΄ΡΠ΅ΠΊΡ Π·Π° ΠΌΡΠ·ΠΈΡΠΊΠ΅ ΡΡΡΠ΄ΠΈΡΠ΅ ΠΠ°ΡΠΈΠΎΠ½Π°Π»Π½ΠΎΠ³ ΠΈ ΠΠ°ΠΏΠΎΠ΄ΠΈΡΡΡΠΈΡΠ°ΡΠΎΠ²ΠΎΠ³ ΡΠ½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅ΡΠ° Ρ ΠΡΠΈΠ½ΠΈ. ΠΠ°ΡΡΡΠΏΡΠ΅Π½Π΅ ΡΠ΅ΠΌΠ΅ ΠΏΠΎΠΊΡΠΈΠ²Π°ΡΡ ΡΠΈΡΠΎΠΊ ΡΠΏΠ΅ΠΊΡΠ°Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΡΠΈΠΊΠ΅ Ρ Π²Π΅Π·ΠΈ ΡΠ° ΡΠ²ΠΈΠΌ ΡΡΠΈΠΌΠ° ΡΠ°Π·Π°ΠΌΠ° ΡΡΠ²Π°ΡΠ°Π»Π°ΡΡΠ²Π° Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ (ΡΡΡΠΊΠ°, Π½Π΅ΠΎΠΊΠ»Π°ΡΠΈΡΠ½Π° ΠΈ ΡΠ΅ΡΠΈΡΠ°Π»Π½Π°), ΡΠΊΡΡΡΡΡΡΡΠΈ ΠΏΡΠΈ ΡΠΎΠΌΠ΅ ΠΈ ΠΏΠΈΡΠ°ΡΠ° Π΅ΡΡΠ΅ΡΠΈΠΊΠ΅, ΠΊΠ°ΠΎ ΠΈ ΡΡΠΈΡΠ°ΡΠ° ΠΈ ΡΠ΅ΡΠ΅ΠΏΡΠΈΡΠ΅ ΡΠ΅Π³ΠΎΠ²ΠΎΠ³ Π΄Π΅Π»Π°.
Π‘ΡΠ°ΠΌΠ°ΡΠΈΡ ΠΠΎΡ
ΠΈΠΎΡ ΠΈΠ·Π½ΠΎΠ²Π° ΠΎΡΠ²Π°ΡΠ° ΠΏΠΈΡΠ°ΡΠ΅ Π²Π΅Π·Π΅ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ Ρ ΡΡΡΠΊΠΈΠΌ ΡΠΎΠ»ΠΊΠ»ΠΎΡΠΎΠΌ, ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡΠ°Π»ΠΈΠ·ΡΡΡΡΠΈ Π΄Π΅Π»Π° ΠΎΠ²ΠΎΠ³ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ° ΠΈΠ· ΡΠ°ΠΊΠΎΠ·Π²Π°Π½ΠΎΠ³ βΡΡΡΠΊΠΎΠ³β ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Ρ ΡΠ΅ΡΠ΅ΡΠ΅Π½ΡΠ°ΠΌΠ° Π½Π° ΠΈΡΡΠΎΡΠΈΡΠ°Ρ ΡΡΡΠΊΠ΅ ΡΠΎΠ»ΠΊΠ»ΠΎΡΠΈΡΡΠΈΠΊΠ΅. ΠΠΎΡ
ΠΈΠΎΡ Π·Π°ΠΊΡΡΡΡΡΠ΅ Π΄Π° ΡΠ΅ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΈ ΠΏΡΠ°ΡΠΈΠΎ ΠΏΡΡ ΡΠ²ΠΎΡΠΈΡ
ΠΏΡΠ΅ΡΡ
ΠΎΠ΄Π½ΠΈΠΊΠ° ΠΈΠ· 19. Π²Π΅ΠΊΠ° (ΠΠ»ΠΈΠ½ΠΊΠ° ΠΈ ΠΠ΅ΡΠΎΡΠΈΡΠ°) Ρ ΡΠΏΠΎΡΡΠ΅Π±ΠΈ ΡΠΎΠ»ΠΊΠ»ΠΎΡΠ½ΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ° Π·Π° ΡΠ²ΠΎΡΠ΅ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΡΠ΅, Π°Π»ΠΈ, Π·Π° ΡΠ°Π·Π»ΠΈΠΊΡ ΠΎΠ΄ ΡΠΈΡ
, Π½ΠΈΡΠ΅ ΠΊΠΎΡΠΈΡΡΠΈΠΎ ΡΡΡΠ΄ΠΈΡΠ΅ Π½Π°ΡΡΠ°ΡΠ°Π»Π΅ Ρ ΡΠ΅Π³ΠΎΠ²ΠΎΠΌ Π²ΡΠ΅ΠΌΠ΅Π½Ρ; ΡΡΠΎΠ³Π°, Π½ΠΈΡΠ΅ ΡΠ΅ ΡΠΏΡΡΡΠΈΠΎ Ρ Π΅ΠΊΡΡΠ΅Π½Π·ΠΈΠ²Π½Ρ ΠΈ Π΄ΡΠ±ΠΈΠ½ΡΠΊΡ ΠΏΡΠ΅ΡΡΠ°Π³Ρ ΠΏΠΎΡΡΠΎΡΠ΅ΡΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ° ΠΎ ΡΡΡΠΊΠΎΠΌ ΡΠΎΠ»ΠΊΠ»ΠΎΡΡ, Π²Π΅Ρ ΡΠ΅ ΡΠΌΠ΅ΡΡΠΎ ΡΠΎΠ³Π° ΠΎΡΠ»Π°ΡΠ°ΠΎ Π½Π° ΠΏΡΠΎΠ²Π΅ΡΠ΅Π½Π΅ ΠΈΠ·Π²ΠΎΡΠ΅ ΠΈ ΡΡΡΠ΄ΠΈΡΠ΅ ΠΈΠ· 19. Π²Π΅ΠΊΠ°. Π§Π»Π°Π½Π°ΠΊ ΠΠ²Π°Π½Π° ΠΡΠ΄ΠΈΡΠ° ΠΎΡΠ²Π΅ΡΡΠ°Π²Π° Π΄Π΅ΠΎ ΡΡΠ²Π°ΡΠ°Π»Π°ΡΡΠ²Π° Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ Ρ ΡΠΎΠΊΡΡΠΎΠΌ Π½Π° ΡΠ΅Π³ΠΎΠ²Π° Π΄ΡΡ
ΠΎΠ²Π½Π° Π΄Π΅Π»Π° ΠΈΠ· ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅ ΡΠ΅Π³ΠΎΠ²ΠΎΠ³ ΠΏΠΎΠ·Π½Π°Π²Π°ΡΠ° ΡΠΈΠ»ΠΎΠ·ΠΎΡΠΈΡΠ΅ ΠΠ°ΠΊΠ° ΠΠ°ΡΠΈΡΠ΅Π½Π°, Π° Ρ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ ΡΠΈΠ»ΠΎΠ·ΠΎΡΡΠΊΠΈΡ
ΠΏΠΎΡΡΠ΅ΡΠ° Ρ ΠΌΠ΅ΡΡΡΠ°ΡΠ½ΠΎΡ Π€ΡΠ°Π½ΡΡΡΠΊΠΎΡ. ΠΡΠ΄ΠΈ ΠΎΠ±ΡΠ°ΡΠ° ΠΏΠΎΡΠ΅Π±Π½Ρ ΠΏΠ°ΠΆΡΡ Π½Π° ΡΠΎ ΠΊΠ°ΠΊΠΎ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΈ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠΈΡΠ° ΠΠ°ΡΠΈΡΠ΅Π½ΠΎΠ²Ρ ΠΈΠ΄Π΅ΡΡ homo faber-a, βΡΠΎΠ²Π΅ΠΊΠ°-ΡΠ²ΠΎΡΡΠ°β. ΠΡΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½ΠΎ, ΠΎΠ½ Π½Π΅ Π·Π°Π½Π΅ΠΌΠ°ΡΡΡΠ΅ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠΎΠ²ΠΎ ΡΡΡΠΊΠΎ ΠΏΠΎΡΠ΅ΠΊΠ»ΠΎ, Π·Π°ΠΊΡΡΡΡΡΡΡΠΈ Π΄Π° ΡΡ Π΄Π΅Π»Π° ΠΊΠΎΡΠ° ΡΡ ΠΎΠ²Π΄Π΅ ΡΠ°Π³Π»Π΅Π΄Π°Π²Π°Π½Π° ΡΠ΅Π΄Π½Π°ΠΊΠΎ ΡΡΠ΅ΠΌΠ΅ΡΠ΅Π½Π° Ρ ΡΡΡΠΊΠΎΠΌ ΠΏΠΎΡΠ΅ΠΊΠ»Ρ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ ΠΊΠ°ΠΎ ΠΈ Ρ ΡΠ΅Π³ΠΎΠ²ΠΈΠΌ ΠΈΡΠΊΡΡΡΠ²ΠΈΠΌΠ° Π½Π° ΠΠ°ΠΏΠ°Π΄Ρ. ΠΠ°ΡΠ΅ΡΠΈΠ½Π° ΠΠ΅Π²ΠΈΠ΄Ρ Π΄Π°ΡΠ΅ Π½ΠΎΠ²ΠΈ ΠΏΠΎΠ³Π»Π΅Π΄ Π½Π° ΠΠΎΠ΅ΡΠΈΠΊΡ ΠΌΡΠ·ΠΈΠΊΠ΅, Π° Π½Π°ΡΠΎΡΠΈΡΠΎ Π½Π° ΠΏΠΈΡΠ°ΡΠ΅ Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠ° ΠΡΠ΅ΡΠ° Π‘ΡΠ²ΡΠΈΠ½ΡΠΊΠΎΠ³ ΠΎΠ²ΠΎΠΌ ΠΏΠΎΠ΄ΡΡ
Π²Π°ΡΡ. ΠΠ΅Π½ΠΎ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΊΡΠ΅ΡΠ΅ ΡΠ΅ ΠΈΠ·Π²Π°Π½ ΠΎΡΠ΅ΠΊΠΈΠ²Π°Π½ΠΈΡ
ΠΌΠ΅ΡΡΠ° Π·Π° ΠΏΠΎΡΠΌΠ°ΡΡΠ°ΡΠ΅, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ ΠΈΠ·Π²Π°Π½ ΠΏΠ΅ΡΠΎΠ³ ΠΏΠΎΠ³Π»Π°Π²ΡΠ° (ΠΊΠΎΡΠ΅ ΡΠ΅ Π½Π°ΠΏΠΈΡΠ°ΠΎ Π‘ΡΠ²ΡΠΈΠ½ΡΠΊΠΈ) ΠΈ Π΄ΠΎΠ±ΡΠΎ ΠΏΠΎΠ·Π½Π°ΡΠ΅ ΡΠ΅ΡΠ΅ΡΠ΅Π½ΡΠ΅ ΠΊΠ° ΠΈΠ΄Π΅ΡΠ°ΠΌΠ° Π‘ΡΠ²ΡΠΈΠ½ΡΠΊΠΎΠ³ ΠΎ ΠΌΡΠ·ΠΈΡΠΈ ΠΈ Π²ΡΠ΅ΠΌΠ΅Π½Ρ. ΠΠ° ΡΠ°Ρ Π½Π°ΡΠΈΠ½ ΡΠ΅ ΠΠΎΠ΅ΡΠΈΠΊΠ° ΠΈΠ½ΡΡΠΈΠ³Π°Π½ΡΠ½ΠΎ ΠΏΠΎΡΡΠ°ΡΠΈ Π½Π°ΡΠΌΠ°ΡΠ΅ ΠΎΡΠ΅ΠΊΠΈΠ²Π°Π½Π° ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ° Π·Π° ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΡΡ ΠΈ Π΄ΠΈΡΠ΅ΠΌΠΈΠ½Π°ΡΠΈΡΡ ΠΏΠΎΠ·ΠΈΡΠΈΡΠ° ΠΏΠΎΠ²Π΅Π·Π°Π½ΠΈΡ
Ρ ΠΎΠ΄ΡΠ΅ΡΠ΅Π½ΠΈΠΌ Π½ΠΈΡΠΈΠΌΠ° βΠ΅Π²ΡΠΎΠ°Π·ΠΈΡΡΡΠ²Π°β, ΡΡΡΠΊΠΎΠ³ Π΅ΠΌΠΈΠ³ΡΠ°Π½ΡΡΠΊΠΎΠ³ ΠΈΠ½ΡΠ΅Π»Π΅ΠΊΡΡΠ°Π»Π½ΠΎΠ³ ΠΈ ΠΏΠΎΠ»ΠΈΡΠΈΡΠΊΠΎΠ³ ΠΏΠΎΠΊΡΠ΅ΡΠ°, Ρ ΠΊΠΎΡΠΈΠΌ ΡΠ΅ Π‘ΡΠ²ΡΠΈΠ½ΡΠΊΠΈ Π±ΠΈΠΎ Π±Π»ΠΈΠ·Π°ΠΊ. ΠΡΠΈΡΡΠΎΡ Π€Π»Π°ΠΌ ΡΠΎΠΊΡΡΠΈΡΠ°ΠΎ ΡΠ΅ Π½Π° ΠΊΠ°ΡΠ½ΠΎ ΡΡΠ²Π°ΡΠ°Π»Π°ΡΡΠ²ΠΎ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ ΠΈ ΡΠ°Π·ΠΌΠ°ΡΡΠ°ΠΎ Π³Π° ΠΈΠ· ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅ Π΅ΠΊΡΠΏΡΠ΅ΡΠΈΠ²Π½ΠΎΡΡΠΈ. ΠΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎ, ΠΎΠ²Π°Ρ Π°ΡΡΠΎΡ ΡΠ΅ ΠΈΡΡΠ°ΠΊΠ°ΠΎ Π΅ΠΊΡΠΏΡΠ΅ΡΠΈΠ²Π½Π΅, ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ΅ ΠΈ ΡΠ°ΠΌΠΎΡΠ΅ΡΠ΅ΡΠ΅Π½ΡΠΈΡΠ°Π»Π½Π΅ Π΄ΠΈΠΌΠ΅Π½Π·ΠΈΡΠ΅ Ρ ΠΊΠ°ΡΠ½ΠΈΠΌ Π΄Π΅Π»ΠΈΠΌΠ°, ΠΊΠΎΡΠ΅ ΡΠ΅ Ρ ΡΠΈΠΌΠ° ΠΏΠΎΡΠ°Π²ΡΡΡΡ Ρ Π½Π°ΡΠΎΡΠΈΡΠΎΠΌ ΡΠ°ΡΠ½ΠΎΡΠΎΠΌ ΠΈ Π΄Π΅Π»ΠΈΠΌΠΈΡΠ½ΠΎ ΠΏΡΠΎΡΠΈΠ²ΡΠ΅ΡΠ΅ ΡΠΎΠ±ΠΈΡΠ°ΡΠ΅Π½ΠΈΠΌ ΠΎΡΠ΅Π½Π°ΠΌΠ° ΠΎΠ²Π΅ ΠΌΡΠ·ΠΈΠΊΠ΅ ΠΊΠ°ΠΎ Π°ΠΏΡΡΡΠ°ΠΊΡΠ½Π΅ ΠΈ ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠ²ΠΈΡΡΠΈΡΠΊΠ΅, Π° ΡΠ°ΠΊΠΎΡΠ΅ ΠΈΠ·Π°Π·ΠΈΠ²Π°ΡΡ ΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠΎΠ²Π΅ Π»ΠΈΡΠ½Π΅ ΠΈΠ·ΡΠ°Π²Π΅. Π‘ ΡΠ»Π°Π½ΠΊΠΎΠΌ ΠΠ΄Π²Π°ΡΠ΄Π° ΠΠ΅ΠΌΠ±Π΅Π»Π° ΠΎΡΡΠ°ΡΠ΅ΠΌΠΎ Ρ ΠΏΠΎΡΠ»Π΅ΡΠ°ΡΠ½ΠΎΠΌ Π΄ΠΎΠ±Ρ, Π°Π»ΠΈ ΡΠ΅ ΡΠΎΠΊΡΡ ΠΏΠΎΠΌΠ΅ΡΠ° Ρ Π΄Π΅Π»Π° Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ ΠΏΠΎ ΡΠ΅Π±ΠΈ Π½Π° ΡΡΠΈΡΠ°Ρ ΡΠ΅Π³ΠΎΠ²ΠΎΠ³ ΡΠ°Π΄Π° Π½Π° ΡΡΠ°Π½ΠΊΠΎΡΠΎΠ½Ρ ΠΏΠΎΡΠ»Π΅ΡΠ°ΡΠ½Ρ Π°Π²Π°Π½Π³Π°ΡΠ΄Ρ, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ Π½Π° ΠΡΠ΅ΡΠ° ΠΡΠ»Π΅Π·Π°, ΠΠ°Π½Π° ΠΠ°ΡΠ°ΠΊΠ°, ΠΠ½ΡΠΈΡΠ° ΠΡΡΠ΅ΡΠ° ΠΈ ΠΠΈΡΠ΅Π»Π° Π€ΠΈΠ»ΠΈΠΏΠΎΠ°. ΠΠ΅ΠΌΠ±Π΅Π»ΠΎΠ²Π° Π°Π½Π°Π»ΠΈΠ·Π° ΠΈΠ·Π²Π»Π°ΡΠΈ Π½Π° ΠΏΠΎΠ²ΡΡΠΈΠ½Ρ ΡΡΠΈΡΠ°Ρ ΠΊΠΎΡΠΈ ΡΠ΅ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΈ ΠΈΠΌΠ°ΠΎ Π½Π° ΠΎΠ²Π΅ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ΅ Π½Π° Π½ΠΈΠ²ΠΎΠΈΠΌΠ° ΡΠΈΡΠΌΠΈΡΠΊΠ΅ ΠΈΠ½ΠΎΠ²Π°ΡΠΈΡΠ΅, ΡΠΏΠΎΡΡΠ΅Π±Π΅ Π·Π²ΡΡΠ½ΠΎΡΡΠΈ, Ρ
Π°ΡΠΌΠΎΠ½ΠΈΡΠ°, ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ°Π»Π½Π΅ Π±ΠΎΡΠ΅, ΠΌΡΠ·ΠΈΡΠΊΠ΅ ΡΠΎΡΠΌΠ΅, ΠΊΠ°ΠΎ ΠΈ ΠΏΠΎΠ»Π°ΡΠΈΡΠ΅ΡΠ° Π²ΠΈΡΠΈΠ½Π΅ ΡΠΎΠ½Π°.
ΠΠΎΡΠ»Π΅Π΄ΡΠ° Π΄Π²Π° ΡΠ»Π°Π½ΠΊΠ° ΠΏΡΠΈΠ»Π°Π·Π΅ ΡΠ°Π΄Ρ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ ΠΈΠ· ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π΅ Π΅ΡΡΠ΅ΡΠΈΠΊΠ΅, Ρ ΡΠ΅ΡΠ΅ΡΠ΅Π½ΡΠ°ΠΌΠ° ΠΊΠ° ΡΠΈΠ»ΠΎΠ·ΠΎΡΠΈΡΠΈ ΡΠ΅Π³ΠΎΠ²ΠΎΠ³ ΡΠ°Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΠΊΠ°, Π₯Π΅Π»ΠΌΡΡΠ° ΠΠ»Π΅ΡΠ½Π΅ΡΠ°. ΠΠ°ΠΊΠΎΠ²ΠΎΡ Π¨ΡΠ°ΡΠ½Ρ
Π°ΡΠ΅Ρ ΡΠ°ΡΠΏΡΠ°Π²ΡΠ° ΠΎ Π·Π½Π°ΡΠ΅ΡΡ ΡΠ΅Π»Π΅ΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΏΠ»Π΅ΡΠ° Ρ ΡΡΠ²Π°ΡΠ°Π»Π°ΡΡΠ²Ρ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³, ΠΎΠ΄Π»Π°Π·Π΅ΡΠΈ ΠΈΠ·Π²Π°Π½ ΠΈΡΡΠΎΡΠΈΡΡΠΊΠΈ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ²Π°Π½ΠΎΠ³ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠΎΠ²Π°ΡΠ° ΠΎΠ²ΠΎΠ³ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ° Π·Π° Π±Π°Π»Π΅ΡΡΠΊΡ ΠΌΡΠ·ΠΈΠΊΡ. ΠΠ°Π»Π΅ΡΡΠΊΠ° ΠΌΡΠ·ΠΈΠΊΠ° Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³, ΠΊΠ°ΠΊΠΎ Π¨ΡΠ°ΡΠ½Ρ
Π°ΡΠ΅Ρ ΠΏΠΎΠΊΠ°Π·ΡΡΠ΅, ΠΏΠΎΡΡΠΈΠΆΠ΅ ΡΡΠ°ΡΠ΅ βΠΏΠΎΡΡΠ΅Π΄ΠΎΠ²Π°Π½Π΅ Π½Π΅ΠΏΠΎΡΡΠ΅Π΄Π½ΠΎΡΡΠΈβ, ΠΎΠ΄ΡΠΆΠ°Π²Π°ΡΡΡΠΈ ΡΠ°ΠΊΠΎ Π΄ΠΈΡΡΠ°Π½ΡΡ Ρ ΠΎΠ΄Π½ΠΎΡΡ Π½Π° ΡΡΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈ ΠΈΠ·ΡΠ°Π·, Π±Π΅Π· ΠΊΠΎΡΠ΅Π³, ΠΏΠ°ΠΊ, ΠΏΠΎΡΡΠ°ΡΠ΅ Π°ΠΏΡΡΡΠ°ΠΊΡΠ½Π°. ΠΠΎΠ½Π°ΡΠ½ΠΎ, ΠΠ°ΡΠΊΠΎΡ Π¦Π΅ΡΠΎΡ Π½ΡΠ΄ΠΈ Π½ΠΎΠ²Ρ ΠΊΡΠΈΡΠΈΠΊΡ ΠΠ΄ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΡΠΈΡΠΈΠΊΠ΅ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³, ΡΠ΅ΡΠ΅ΡΠΈΡΠ°ΡΡΡΠΈ Π½Π° ΠΠ»Π΅ΡΠ½Π΅ΡΠΎΠ²Ρ ΡΠΈΠ»ΠΎΠ·ΠΎΡΡΠΊΡ Π°Π½ΡΡΠΎΠΏΠΎΠ»ΠΎΠ³ΠΈΡΡ. ΠΠ½, ΡΡΠΎΠ³Π°, ΠΈΠ·Π°Π·ΠΈΠ²Π° ΠΠ΄ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠΎΠ³Π»Π΅Π΄Π΅ ΠΊΠΎΡΠΈ Π‘ΡΡΠ°Π²ΠΈΠ½ΡΠΊΠΎΠ³ ΡΠ²ΡΡΡΠ°Π²Π°ΡΡ Ρ Π½Π΅Ρ
ΡΠΌΠ°Π½Π΅ ΠΈ ΠΏΡΠΈΠΌΠΈΡΠΈΠ²Π½Π΅, ΠΏΠΎΠΊΠ°Π·ΡΡΡΡΠΈ ΡΠΌΠ΅ΡΡΠΎ ΡΠΎΠ³Π° Π΄Π° ΡΠ΅Π³ΠΎΠ²Π° ΠΌΡΠ·ΠΈΠΊΠ° Π΄ΠΎΡΡΠΈΠΆΠ΅, Ρ ΠΈΡΡΠΎΡΠΈΡΡΠΊΠΈ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΈΠΌ ΠΌΠΎΠ΄Π΅ΡΠ½ΠΈΠΌ ΡΠ΅ΡΠΌΠΈΠ½ΠΈΠΌΠ°, ΠΊΠΎΠ½ΡΡΠΈΡΡΡΠΈΠ²Π½Ρ ΡΠ΅ΡΠ»Π΅ΠΊΡΠΈΠ²Π½ΠΎΡΡ ΡΡΠ΄ΡΠΊΠΎΠ³ ΠΎΡΠ΅Π»ΠΎΡΠ²ΠΎΡΠ΅Π½ΠΎΠ³ ΡΡΠ°ΡΠ°.
Π ΡΠ±ΡΠΈΠΊΠ° Varia ΠΎΠ²ΠΎΠΌ ΡΠ΅ ΠΏΡΠΈΠ»ΠΈΠΊΠΎΠΌ Π½Π΅ΡΡΠΎ ΡΠ°ΠΆΠ΅ΡΠΈΡΠ° β Π΄ΠΎΠ½ΠΎΡΠΈ ΡΡΠΈ ΡΡΡΠ΄ΠΈΡΠ΅, Π°Π»ΠΈ ΡΡ ΡΠΈΠΌΠ° ΠΌΠ°ΡΠΊΠΈΡΠ°Π½Π° ΡΠ°Π·Π»ΠΈΡΠΈΡΠ° ΠΏΠΎΡΠ° ΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΡΠΊΠΈΡ
ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ°. ΠΠΎΡΠ°Π½Π° Π Π°Π΄ΠΎΠ²Π°Π½ΠΎΠ²ΠΈΡ ΡΡΡΠ΄ΠΈΠΎΠ·Π½ΠΎ ΡΠ΅ Π±Π°Π²ΠΈΠ»Π° ΠΏΡΠΎΡΡΠ°Π²Π°ΡΠ΅ΠΌ Π΅ΠΊΡΠΏΡΠ΅ΡΠΈΠ²Π½ΠΈΡ
ΡΡΠ΅Π΄ΡΡΠ°Π²Π° Ρ Π΄Π΅Π»ΠΈΠΌΠ° Π·Π° Π³Π»Π°Ρ ΡΡΠΏΡΠΊΠΎΠ³ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ° ΠΡΠ³Π° ΠΠ°ΡΠΊΠΎΠ²ΠΈΡΠ°, Π°Π»ΠΈ ΠΈ ΠΏΠΈΡΠ°ΡΠΈΠΌΠ° ΠΈΠ· Π΄ΠΎΠΌΠ΅Π½Π° ΠΎΠ΄Π½ΠΎΡΠ° ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ° ΠΈ Π²ΠΎΠΊΠ°Π»Π½ΠΎΠ³ ΠΈΠ·Π²ΠΎΡΠ°ΡΠ°. Π¦ΠΈΡ ΠΎΠ²ΠΎΠ³ ΠΏΠΎΠ΄ΡΡ
Π²Π°ΡΠ° Π±ΠΈΠΎ ΡΠ΅ Π΄Π° ΠΏΡΡΠ΅ΠΌ Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΎΠ΄Π°Π±ΡΠ°Π½ΠΈΡ
ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΡΠ° Π΄ΠΎΠΏΡΠΈΠ½Π΅ΡΠ΅ ΠΎΡΠ²Π΅ΡΡΠ°Π²Π°ΡΡ ΠΠ°ΡΠΊΠΎΠ²ΠΈΡΠ΅Π²ΠΎΠ³ Π΅ΠΊΠ»Π΅ΠΊΡΠΈΡΠ½ΠΎΠ³ ΡΡΠΈΠ»Π°, ΠΊΠ°ΠΎ ΠΈ Π΄Π° ΡΠ΅ ΠΎΠ½ ΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠ° Ρ ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½Π΅ ΠΊΡΡΠ³ΠΎΠ²Π΅ ΡΡΠ²Π°ΡΠ°Π»Π°ΡΠ° ΠΊΠΎΡΠΈ ΡΠ΅ Π³Π»Π°ΡΠΎΠΌ Π±Π°Π²Π΅ Π½Π° Π½Π΅ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½Π΅ Π½Π°ΡΠΈΠ½Π΅. ΠΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΠ°ΡΠΈΡΠ΅ ΠΠΈΠ½ΠΎΠ² ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½ΠΎ ΠΎΠ²ΠΎΠΌ ΠΏΡΠΈΠ»ΠΈΠΊΠΎΠΌ ΠΎΠ΄Π½ΠΎΡΠΈ ΡΠ΅ Π½Π° ΠΊΠΈΠ½Π΅ΡΡΠ΅ΡΠΈΡΠΊΠ΅ Π³Π΅ΡΡΠΎΠ²Π΅, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ ΡΠ΅Π»Π΅ΡΠ½Π΅ ΠΏΠΎΠΊΡΠ΅ΡΠ΅ ΠΈΠ·Π²ΠΎΡΠ°ΡΠ° ΡΠΎΠΊΠΎΠΌ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ°, Π° ΡΠΊΡΡΡΡΡΡΡΠΈ ΡΠΎΠΏΡΡΠ²Π΅Π½Π° ΠΏΠΈΡΠ°Π½ΠΈΡΡΠΈΡΠΊΠ° ΠΈΡΠΊΡΡΡΠ²Π°, ΠΏΠΎΡΠ΅Π±Π½Ρ ΡΠ΅ ΠΏΠ°ΠΆΡΡ ΠΏΠΎΡΠ²Π΅ΡΠΈΠ»Π° ΠΠ΅ΡΠΎΠ²Π΅Π½ΠΎΠ²ΠΈΠΌ ΠΊΠ»Π°Π²ΠΈΡΡΠΊΠΈΠΌ ΡΠΎΠ½Π°ΡΠ°ΠΌΠ°. ΠΠ±ΡΠ°ΡΡΠ΅ΡΠ΅ ΡΡΠΈΡΠ°ΡΠ° ΠΊΠ°ΡΠ°ΠΊΡΠ΅ΡΠ° ΡΠΈΠ·ΠΈΡΠΊΠΈΡ
ΠΏΠΎΠΊΡΠ΅ΡΠ° ΡΠ΅Π»Π° Π½Π° ΠΌΡΠ·ΠΈΡΠΊΠΎ Π΄Π΅Π»ΠΎ ΡΠΊΡΡΡΠΈΠ»ΠΎ ΡΠ΅ ΠΎΡΠ²ΡΡ Π½Π° ΡΡΠ²Π°ΡΠ°ΡΠ΅ ΠΌΡΠ·ΠΈΡΠΊΠΎΠ³ Π΄Π΅Π»Π°, Π° Ρ Π΄ΡΡΠ³Π΅ ΡΡΡΠ°Π½Π΅ ΠΈ Π½Π° ΡΠ΅Π½Π·Π°ΡΠΈΡΠ΅ ΡΠΎΠΊΠΎΠΌ ΡΠ΅Π³ΠΎΠ²ΠΎΠ³ ΠΈΠ·Π²ΠΎΡΠ΅ΡΠ°, ΡΡΠΎ ΡΠ΅ ΡΡΠ³Π΅ΡΠΈΡΠ°Π»ΠΎ ΡΠΈΡΠΈ Π·Π°ΠΊΡΡΡΠ°ΠΊ ΠΎ ΠΏΠΎΡΠ΅Π±Π½ΠΎΡ Π²Π°ΠΆΠ½ΠΎΡΡΠΈ ΡΠ°Π·ΡΠΌΠ΅Π²Π°ΡΠ° βΠΊΠΈΠ½Π΅ΡΠΈΡΠΊΠ΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅ ΠΌΡΠ·ΠΈΠΊΠ΅β. ΠΠΎΡΠ»Π΅Π΄ΡΠΈ Ρ ΠΎΠ²ΠΎΠΌ Π΄Π΅Π»Ρ ΡΠ°ΡΠΎΠΏΠΈΡΠ° ΡΠ΅ ΡΠ»Π°Π½Π°ΠΊ ΠΠΈΠ½Π΅ ΠΠΎΡΠ²ΠΎΠ΄ΠΈΡ ΠΠΈΠΊΠΎΠ»ΠΈΡ, ΠΏΠΎΡΠ²Π΅ΡΠ΅Π½ ΠΌΡΠ·ΠΈΡΠΊΠΈΠΌ ΠΊΡΠΈΡΠΈΠΊΠ°ΠΌΠ° ΠΠ΅ΡΡΠ° ΠΠΈΠ½Π³ΡΠ»ΡΠ°, ΠΊΠ°ΠΎ ΠΏΠΎΡΠ΅Π±Π½ΠΎ Π²Π°ΠΆΠ½ΠΎΠΌ Π΄Π΅Π»Ρ ΡΠ΅Π³ΠΎΠ²ΠΎΠ³ Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠ° ΡΡΠΏΡΠΊΠΎΡ ΠΊΡΠ»ΡΡΡΠ½ΠΎΡ ΠΈΡΡΠΎΡΠΈΡΠΈ ΠΈ ΠΌΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠΈ. ΠΠ½Π°Π»ΠΈΠ·ΠΈΡΠ°Π½ΠΈ ΡΡ ΡΠ΅ΠΊΡΡΠΎΠ²ΠΈ ΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½ΠΈ Ρ ΡΠ°ΡΠΎΠΏΠΈΡΡ ΠΠΈΡΠ°ΠΎ, ΡΠΊΠ°Π·Π°Π½ΠΎ ΡΠ΅ Π½Π° ΠΠΈΠ½Π³ΡΠ»ΡΠ΅Π² Π½Π°ΡΠΈΠ½ ΠΌΠΈΡΡΠ΅ΡΠ° ΠΈ ΠΌΠ΅ΡΠΎΠ΄ ΡΠ°Π΄Π°, Π° ΠΊΠΎΠΌΠΏΠ°ΡΠ°ΡΠΈΠ²Π½ΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·ΠΎΠΌ Ρ ΠΊΡΠΈΡΠΈΠΊΠ°ΠΌΠ° Π΄ΡΡΠ³ΠΈΡ
Π°ΡΡΠΎΡΠ° Π΄Π°ΡΠ° ΡΠ΅ ΠΎΡΠ½ΠΎΠ²Π° Π·Π° ΡΠ΅Π³ΠΎΠ²ΠΎ ΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½ΠΈΡΠ°ΡΠ΅ Ρ ΡΡΠΏΡΠΊΠΎΡ ΠΌΡΠ·ΠΈΡΠΊΠΎΡ ΠΊΡΠΈΡΠΈΡΠΈ Ρ ΠΏΠ΅ΡΠΈΠΎΠ΄Ρ ΠΈΠ·ΠΌΠ΅ΡΡ Π΄Π²Π°ΡΡ ΡΠ²Π΅ΡΡΠΊΠΈΡ
ΡΠ°ΡΠΎΠ²Π°, ΠΊΠ°Π΄Π° ΡΠ΅ ΠΏΠΎΠΌΠ΅Π½ΡΡΠΈ ΡΠ°ΡΠΎΠΏΠΈΡ ΠΈΠ·Π»Π°Π·ΠΈΠΎ.
ΠΡΠΈΠ»ΠΎΠ·ΠΈ Ρ ΡΡΠ±ΡΠΈΡΠΈ ΠΠ°ΡΡΠ½Π° ΠΊΡΠΈΡΠΈΠΊΠ° ΠΈ ΠΏΠΎΠ»Π΅ΠΌΠΈΠΊΠ° ΠΎΠ΄Π½ΠΎΡΠ΅ ΡΠ΅ Π½Π° Π½Π΅Π΄Π°Π²Π½ΠΎ ΠΎΠ΄ΡΠΆΠ°Π½ Π½Π°ΡΡΠ½ΠΈ ΡΠΊΡΠΏ ΠΈ Π½Π° Π·Π±ΠΎΡΠ½ΠΈΠΊ Π·Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΈΠ·ΠΎΡΡΠ°Π»Π° Π΄ΡΠΆΠ½Π° ΠΏΠ°ΠΆΡΠ° Π·Π±ΠΎΠ³ ΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°ΡΠ° ΡΠΎΠΊΠΎΠΌ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΡΠ΅ ΠΊΠΎΠ²ΠΈΠ΄Π°. ΠΠ°ΡΠΈΡΠ° ΠΠ°Π³Π»ΠΎΠ² ΠΏΡΠΈΠΏΡΠ΅ΠΌΠΈΠ»Π° ΡΠ΅ ΠΊΡΠΈΡΠΈΡΠΊΠΈ ΠΎΡΠ²ΡΡ Π½Π° ΠΌΠ΅ΡΡΠ½Π°ΡΠΎΠ΄Π½ΠΈ ΡΠΈΠΌΠΏΠΎΠ·ΠΈΡΡΠΌ ΠΏΠΎΡΠ²Π΅ΡΠ΅Π½ ΡΠ°Π½ΠΎΡ Π΄ΠΈΡΠΊΠΎΠ³ΡΠ°ΡΡΠΊΠΎΡ ΠΈΠ½Π΄ΡΡΡΡΠΈΡΠΈ, Π°ΠΊΡΡΠ΅Π»Π½ΠΎΡ ΡΠ΅ΠΌΠΈ Ρ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΡ ΡΡΡΠ΄ΠΈΡΠ° ΠΌΠ΅Π΄ΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡΠ΅ ΠΈ ΠΈΠ½Π΄ΡΡΡΡΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΡΠ΅ ΠΌΡΠ·ΠΈΠΊΠ΅, ΠΊΠΎΡΠΈ ΡΡ ΠΎΡΠ³Π°Π½ΠΈΠ·ΠΎΠ²Π°Π»Π΅ Ρ
ΡΠ²Π°ΡΡΠΊΠ΅ ΠΊΠΎΠ»Π΅Π³Π΅, ΠΌΠ°ΡΡΠ° ΠΎΠ²Π΅ Π³ΠΎΠ΄ΠΈΠ½Π΅. ΠΠ²Π°Ρ ΠΏΡΠΈΠ»ΠΎΠ³ Π½Π°ΡΠΎΡΠΈΡΠΎ ΡΠ΅ Π²Π°ΠΆΠ°Π½, Ρ ΠΎΠ±Π·ΠΈΡΠΎΠΌ Π½Π° ΡΠΎ Π΄Π° ΡΠ΅ Π½Π΅ ΠΎΡΠ΅ΠΊΡΡΠ΅ ΡΠΎΠ±ΠΈΡΠ°ΡΠ΅Π½ ΡΠ΅ΠΌΠ°ΡΡΠΊΠΈ Π·Π±ΠΎΡΠ½ΠΈΠΊ ΡΠ°Π΄ΠΎΠ²Π° ΡΠ²ΠΈΡ
ΡΡΠ΅ΡΠ½ΠΈΠΊΠ° ΡΠΈΠΌΠΏΠΎΠ·ΠΈΡΡΠΌΠ°. ΠΠΈ Π·Π±ΠΎΡΠ½ΠΈΠΊ Rethinking Prokofiev, ΠΊΠΎΡΠΈ ΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΠΈΠΎ ΠΠΈΠ»ΠΎΡ ΠΡΠ°Π»ΠΎΠ²ΠΈΡ, Π½ΠΈΡΠ΅ Π½Π°ΡΡΠ°ΠΎ Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π½Π°ΡΡΠ½ΠΎΠ³ ΡΠΊΡΠΏΠ°, Π²Π΅Ρ ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΠ°Ρ ΠΎΡΠΈΠ³ΠΈΠ½Π°Π»Π½ΠΈΡ
Π°ΡΡ
ΠΈΠ²ΡΠΊΠΈΡ
, Π°Π½Π°Π»ΠΈΡΠΈΡΠΊΠΈΡ
, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ ΠΈΠ·Π²ΠΎΡΠ°ΡΠΊΠΎ-ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΈΡ
ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ° ΠΎΠΏΡΡΠ° ΠΎΠ²ΠΎΠ³ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΎΡΠ°. Π Π΅Ρ ΡΠ΅ ΠΎ ΠΈΠ·Π΄Π°ΡΡ Π½Π° ΠΊΠΎΡΠ΅ΠΌ ΡΡ Π°Π½Π³Π°ΠΆΠΎΠ²Π°Π½ΠΈ Π²ΠΎΠ΄Π΅ΡΠΈ ΡΡΡΡΡΡΠ°ΡΠΈ Ρ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΠΈΡΠΈ ΠΎΡΡΠ²Π°ΡΠ΅ΡΠ° ΠΡΠΎΠΊΠΎΡΡΠ΅Π²Π°, ΠΎΠ΄ ΡΡΠ΅Π΄Π½ΠΈΠΊΠ° Π΄ΠΎ Π°ΡΡΠΎΡΠ°, ΡΠ΅ Π·Π°Π²ΡΠ΅ΡΡΡΠ΅ ΠΏΠΎΡΠ΅Π±Π½Ρ ΠΏΠ°ΠΆΡΡ Π½Π°ΡΡΠ½Π΅ ΡΠ°Π²Π½ΠΎΡΡΠΈ.
Π Π΅Π΄Π°ΠΊΡΠΈΡΠ° ΡΠ°ΡΠΎΠΏΠΈΡΠ° ΠΡΠ·ΠΈΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ° ΡΡΠ΄Π°ΡΠ½ΠΎ Π·Π°Ρ
Π²Π°ΡΡΡΠ΅ Π½Π° ΡΠ°ΡΠ°Π΄ΡΠΈ Π΄Ρ ΠΠ°ΡΠ΅ΡΠΈΠ½ΠΈ ΠΠ΅Π²ΠΈΠ΄Ρ, Π΄ΠΎΡΠ΅Π½ΡΡ Π½Π° ΠΠ΄ΡΠ΅ΠΊΡ Π·Π° ΠΌΡΠ·ΠΈΡΠΊΠ΅ ΡΡΡΠ΄ΠΈΡΠ΅ ΠΠ°ΡΠΈΠΎΠ½Π°Π»Π½ΠΎΠ³ ΠΈ ΠΠ°ΠΏΠΎΠ΄ΠΈΡΡΡΠΈΡΠ°ΡΠΎΠ²ΠΎΠ³ ΡΠ½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅ΡΠ° Ρ ΠΡΠΈΠ½ΠΈ, ΠΊΠΎΡΠ° ΡΠ΅ ΠΎΠ²ΠΎΠΌ ΠΏΡΠΈΠ»ΠΈΠΊΠΎΠΌ ΠΏΡΠ΅ΡΠ·Π΅Π»Π° Π΄ΡΠΆΠ½ΠΎΡΡ Π³ΠΎΡΡΠ΅-ΡΡΠ΅Π΄Π½ΠΈΡΠ΅ Π·Π° ΡΡΠ±ΡΠΈΠΊΡ Π’Π΅ΠΌΠ° Π±ΡΠΎΡΠ°. ΠΠ·ΡΠ·Π΅ΡΠ½Ρ Π·Π°Ρ
Π²Π°Π»Π½ΠΎΡΡ ΠΈΠ·ΡΠ°ΠΆΠ°Π²Π°ΠΌΠΎ ΡΠ²ΠΈΠΌ ΠΊΠΎΠ»Π΅Π³Π°ΠΌΠ° ΠΊΠΎΡΠ΅ ΡΡ ΠΏΡΠΈΡ
Π²Π°ΡΠ°Π»Π΅ ΠΏΠΎΡΠ°ΠΎ ΡΠ΅ΡΠ΅Π½Π·Π΅Π½Π°ΡΠ° ΠΈ Π΄ΠΎΠΏΡΠΈΠ½Π΅Π»Π΅ ΠΊΠ²Π°Π»ΠΈΡΠ΅ΡΡ ΠΏΡΠ±Π»ΠΈΠΊΠΎΠ²Π°Π½ΠΈΡ
ΡΡΡΠ΄ΠΈΡΠ°.The commemoration of the fiftieth anniversary of Igor Stravinskyβs death (1882β1971) remained in the shadows of the covid-19 pandemic, which caused the prolonged response of the scientific community in terms of new readings of the composerβs opus. The Main Theme in the new issue of Muzikologija-Musicology (No. 34) makes a contribution to this response with a series of studies dedicated to Stravinsky, originating from presentations at the Study Day organized in 2021 by the Department of Music Studies of the National and Kapodistrian University of Athens. The topics cover a wide range of issues relating to all three phases of Stravinskyβs creation (the Russian, the Neoclassical and the Serial), including questions of aesthetics, as well as the impact and reception of his work.
Stamatis Zochios revisits the question of Stravinskyβs relationship with Russian folklore, by contextualising the composerβs output of the so-called βRussianβ period with reference to the history of Russian folkloristics. Zochios concludes that Stravinsky followed in the footsteps of his nineteenth-century predecessors (Glinka and the Mighty Five) in drawing on folk sources for his compositions, yet unlike them, he did not make use of studies from his own time; hence, he did not delve into an extensive and in-depth survey of the existing sources of Russian folklore but, instead, relied on established sources and studies from the nineteenth century. Ivan Moodyβs article sheds light on Stravinskyβs output with a focus on his religious works through the perspective of his acquaintance with Jacques Maritainβs philosophy, in the context of the philosophical ferment in Interwar France. Moody pays particular attention to Stravinskyβs interpretation of Maritainβs idea of homo faber, βman the makerβ. At the same time, he does not ignore the composerβs Russian origins, concluding that the works under examination are equally grounded in Stravinskyβs Russian background and his experiences in the West. Katerina Levidou sheds new light on the Poetics of Music, specifically the question of Pierre Souvtchinskyβs contribution. Her examination moves beyond the obvious places to look, namely the fifth chapter (written by Souvtchinsky) and the well-known reference to Souvtchinskyβs ideas on music and time. The Poetics thus emerges as a most unexpected platform for the presentation and dissemination of positions associated with a certain strand of βEurasianismβ, the Russian Γ©migrΓ© intellectual and political movement, with which Souvtchinsky was closely associated.
Christoph Flamm focuses on Stravinskyβs late output and considers it from the perspective of expressiveness. Specifically, he highlights expressive, semantic and self-referential dimensions in the late compositions, which emerge there with particular clarity and partly contradict the usual assessments of this music as abstract and constructivist, but also challenge the composerβs own statements. With Edward Campbellβs article we remain in the post-War era, yet the focus shifts from Stravinskyβs work per se to the impact his output had on the Francophone post-war avant-garde, namely Pierre Boulez, Jean BarraquΓ©, Henri Pousseur and Michel Philippot. Campbellβs analysis brings to the surface the influence Stravinsky had on such composers on the level of rhythmic innovation, and the use of sonorities, harmonies, instrumental colour, musical form as well as pitch polarity.
The last two articles approach Stravinskyβs work from the perspective of aesthetics, with reference specifically to the philosophy of Stravinskyβs contemporary, Helmuth Plessner. Iakovos Steinhauer discusses the meaning of corporeality and dance in Stravinskyβs work, moving beyond Stravinsky's historically-documented interest in ballet music. Stravinskyβs ballet music, as Steinhauer demonstrates, attains a βmediated immediacyβ, thus maintaining a distance from subjective expression, without, however, becoming abstract. Finally, Markos Tsetsos offers a new critique of Adornoβs criticism of Stravinsky with reference to Plessnerβs philosophical anthropology. He, therefore, challenges Adornoβs view that Stravinsky regresses to the inhuman and primitive, demonstrating, instead, that his music affirms, in historically adequate modern terms, the constitutive reflectivity of the human embodied condition.
On this occasion, the Varia section is more concise, to balance out the breadth of the Main Theme. It contains three studies that map out three different fields of musicological research. Bojana RadovanoviΔ has studiously examined the expressive means in the works for the voice of the Serbian composer Jug MarkoviΔ, including the questions from the domain of the relationship between the composer and the vocal performer. By analysing MarkoviΔβs selected works, she aims to illuminate his eclectic style and situate him in the international circles of composers who deal with the voice in non-traditional ways. Marija Dinovβs research presented in this issue deals with kinesthetic gestures, i.e. bodily movements of pianists (including herself) during performances, focusing on the performances of Beethovenβs piano sonatas. The explanation of the influence of physical movements on the musical work includes an overview of the creation of the musical work, and, on the other hand, of the sensations during its performance, which leads to a broader conclusion about the special importance of understanding the βkinetic energy of musicβ. The last article in this section of the journal is Dina VojvodiΔ NikoliΔβs article dedicated to Petar Bingulacβs music criticism, as a particularly important segment of his contribution to Serbian cultural history and musicology. The author analyses Bingulacβs texts published in the journal Misao [Thought] and points to Bingulacβs way of thinking and methods of work, whilst also providing a comparative analysis with the music reviews of other contemporary critics and thus situating Bingulacβs writings within Serbian music criticism from the interwar period, when the journal Misao was published.
Contributions in the section Scientific criticism and polemics refer to the recently held conference and to the collection which has hitherto attracted insufficient attention due to its publication during the covid-19 pandemic. Marija Maglov has prepared a review of the international symposium dedicated to the early recording industry, a current topic in the context of studies of medialisation and industrialisation of music, which was organized by Croatian colleagues in March 2023; this contribution is particularly important considering that the publication of the proceedings of the symposium is not expected. The collection Rethinking Prokofiev, reviewed by MiloΕ‘ BraloviΔ, did not result from a scientific conference either; it is the outcome of original archival, analytical, and performance-interpretive research of Sergei Prokofievβs oeuvre. This collection has gathered together leading experts on Prokofievβs works, from the editors to the authors, and it deserves special attention from the scientific community.
The Editorial Board of the journal Muzikologija-Musicology would like to thank Dr Katerina Levidou, Assistant Professor at the Department of Music Studies of the National and Kapodistrian University of Athens, who served as Guest Editor of the Main Theme. We are very grateful to all colleagues who accepted the roles of peer reviewers and contributed to the quality of published studies
Unsupervised representation learning with recognition-parametrised probabilistic models
We introduce a new approach to probabilistic
unsupervised learning based on the recognitionparametrised model (RPM): a normalised semiparametric hypothesis class for joint distributions
over observed and latent variables. Under the key
assumption that observations are conditionally
independent given latents, the RPM combines
parametric prior and observation-conditioned latent distributions with non-parametric observation marginals. This approach leads to a flexible
learnt recognition model capturing latent dependence between observations, without the need for
an explicit, parametric generative model. The
RPM admits exact maximum-likelihood learning for discrete latents, even for powerful neuralnetwork-based recognition. We develop effective approximations applicable in the continuouslatent case. Experiments demonstrate the effectiveness of the RPM on high-dimensional data,
learning image classification from weak indirect
supervision; direct image-level latent Dirichlet
allocation; and recognition-parametrised Gaussian process factor analysis (RP-GPFA) applied
to multi-factorial spatiotemporal datasets. The
RPM provides a powerful framework to discover
meaningful latent structure underlying observational data, a function critical to both animal and
artificial intelligence
Contributions and applications around low resource deep learning modeling
El aprendizaje profundo representa la vanguardia del aprendizaje automΓ‘tico en multitud de aplicaciones. Muchas de estas tareas requieren una gran cantidad de recursos computacionales, lo que limita su adopciΓ³n en dispositivos integrados. El objetivo principal de esta tesis es estudiar mΓ©todos y algoritmos que permiten abordar problemas utilizando aprendizaje profundo con bajos recursos computacionales. Este trabajo tambiΓ©n tiene como objetivo presentar aplicaciones de aprendizaje profundo en la industria.
La primera contribuciΓ³n es una nueva funciΓ³n de activaciΓ³n para redes de aprendizaje profundo: la funciΓ³n de mΓ³dulo. Los experimentos muestran que la funciΓ³n de activaciΓ³n propuesta logra resultados superiores en tareas de visiΓ³n artificial cuando se compara con las alternativas encontradas en la literatura.
La segunda contribuciΓ³n es una nueva estrategia para combinar modelos preentrenados usando destilaciΓ³n de conocimiento. Los resultados de este capΓtulo muestran que es posible aumentar significativamente la precisiΓ³n de los modelos preentrenados mΓ‘s pequeΓ±os, lo que permite un alto rendimiento a un menor costo computacional.
La siguiente contribuciΓ³n de esta tesis aborda el problema de la previsiΓ³n de ventas en el campo de la logΓstica. Se proponen dos sistemas de extremo a extremo con dos tΓ©cnicas diferentes de aprendizaje profundo (modelos de secuencia a secuencia y transformadores). Los resultados de este capΓtulo concluyen que es posible construir sistemas integrales para predecir las ventas de mΓΊltiples productos individuales, en mΓΊltiples puntos de venta y en diferentes momentos con un ΓΊnico modelo de aprendizaje automΓ‘tico. El modelo propuesto supera las alternativas encontradas en la literatura.
Finalmente, las dos ΓΊltimas contribuciones pertenecen al campo de la tecnologΓa del habla. El primero estudia cΓ³mo construir un sistema de reconocimiento de voz Keyword Spotting utilizando una versiΓ³n eficiente de una red neuronal convolucional. En este estudio, el sistema propuesto es capaz de superar el rendimiento de todos los puntos de referencia encontrados en la literatura cuando se prueba contra las subtareas mΓ‘s complejas. El ΓΊltimo estudio propone un modelo independiente de texto a voz de ΓΊltima generaciΓ³n capaz de sintetizar voz inteligible en miles de perfiles de voz, mientras genera un discurso con variaciones de prosodia significativas y expresivas. El enfoque propuesto elimina la dependencia de los modelos anteriores de un sistema de voz adicional, lo que hace que el sistema propuesto sea mΓ‘s eficiente en el tiempo de entrenamiento e inferencia, y permite operaciones fuera de lΓnea y en el dispositivo.Deep learning is the state of the art for several machine learning tasks. Many of these tasks require large amount of computational resources, which limits their adoption in embedded devices. The main goal of this dissertation is to study methods and algorithms that allow to approach problems using deep learning with restricted computational resources. This work also aims at presenting applications of deep learning in industry.
The first contribution is a new activation function for deep learning networks: the modulus function. The experiments show that the proposed activation function achieves superior results in computer vision tasks when compared with the alternatives found in the literature.
The second contribution is a new strategy to combine pre-trained models using knowledge distillation. The results of this chapter show that it is possible to significantly increase the accuracy of the smallest pre-trained models, allowing high performance at a lower computational cost.
The following contribution in this thesis tackles the problem of sales fore- casting in the field of logistics. Two end-to-end systems with two different deep learning techniques (sequence-to-sequence models and transformers) are pro- posed. The results of this chapter conclude that it is possible to build end-to-end systems to predict the sales of multiple individual products, at multiple points of sale and different times with a single machine learning model. The proposed model outperforms the alternatives found in the literature.
Finally, the last two contributions belong to the speech technology field. The former, studies how to build a Keyword Spotting speech recognition system using an efficient version of a convolutional neural network. In this study, the proposed system is able to beat the performance of all the benchmarks found in the literature when tested against the most complex subtasks.
The latter study proposes a standalone state-of-the-art text-to-speech model capable of synthesizing intelligible voice in thousands of voice profiles, while generating speech with meaningful and expressive prosody variations. The proposed approach removes the dependency of previous models on an additional voice system, which makes the proposed system more efficient at training and inference time, and enables offline and on-device operations
Musiktheorie als interdisziplinΓ€res Fach: 8. Kongress der Gesellschaft fΓΌr Musiktheorie Graz 2008
Im Oktober 2008 fand an der UniversitΓ€t fΓΌr Musik und darstellende Kunst Graz (KUG) der 8. Kongress der Gesellschaft fΓΌr Musiktheorie (GMTH) zum Thema Β»Musiktheorie als interdisziplinΓ€res FachΒ« statt. Die hier vorgelegten gesammelten BeitrΓ€ge akzentuieren Musiktheorie als multiperspektivische wissenschaftliche Disziplin in den Spannungsfeldern Theorie/Praxis, Kunst/Wissenschaft und Historik/Systematik. Die sechs Kapitel ergrΓΌnden dabei die Grenzbereiche zur Musikgeschichte, MusikΓ€sthetik, zur Praxis musikalischer Interpretation, zur kompositorischen Praxis im 20. und 21. Jahrhundert, zur Ethnomusikologie sowie zur Systematischen Musikwissenschaft. Insgesamt 45 AufsΓ€tze, davon 28 in deutscher, 17 in englischer Sprache, sowie die Dokumentation einer Podiumsdiskussion zeichnen in ihrer Gesamtheit einen hΓΆchst lebendigen und gegenwartsbezogenen Diskurs, der eine einzigartige Standortbestimmung des Fachs Musiktheorie bietet.The 8th congress of the Gesellschaft fΓΌr Musiktheorie (GMTH) took place in October 2008 at the University for Music and Dramatic Arts Graz (KUG) on the topic Β»Music Theory and InterdisciplinarityΒ«. The collected contributions characterize music theory as a multi-faceted scholarly discipline at the intersection of theory/practice, art/science and history/system. The six chapters explore commonalties with music history, music aesthetics, musical performance, compositional practice in twentieth- and twenty-first-century music, ethnomusicology and systematic musicology. A total of 45 essays (28 in German, 17 in English) and the documentation of a panel discussion form a vital discourse informed by contemporaneous issues of research in a broad number of fields, providing a unique overview of music theory today. A comprehensive English summary appears at the beginning of all contributions
The westernization of Chinese traditional music and an investigation of Chinese contemporary piano music
Westernization of Chinese traditional music occupies a significant portion of Chinese music history and has had a tremendous influence on Chinese contemporary music.
This dissertation will discuss why almost all contemporary Chinese music has been integrated with Western musical elements. It will also address the evolution of Chinese music, the differences between traditional Chinese music and contemporary Chinese music, the way contemporary Chinese music combines Chinese traditional music with Western music, Chinese peoplesβ views towards Chinese traditional music and contemporary music, and the significance of the above with respect to the preservation of Chinese culture and cultural diversity in the world
Complexity Science in Human Change
This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience
Artificial Intelligence Within the Creative Process of Contemporary Classical Music
This submission consists of nine pieces of original music in addition to a reflective and critical commentary. With one exception, these pieces are each for live performance, written for ensembles and soloists of various descriptions. The exception is an audio-visual work for fixed media.
These pieces were written as part of my practice-based research PhD and concern the relationship between artificial intelligence and my compositional process. They outline the development of my compositional practice, resulting in the piece Silicon for orchestra and electronics which forms a major part of this submission.
The commentary details the algorithms used in the creation of this music, and the aesthetic concerns I developed through working with artificial intelligence. These include the relationship between future and past, authorship, authenticity, musical structuralism, and agency, amongst others. It also describes methods and techniques relating to specific musical elements I developed through working with AI which have had a significant impact on my work.
This research builds upon the areas of research related to my own, especially contemporary classical music, creativity and its relationship to artificial intelligence, machine learning, and algorithmic music practice. It is intended to contribute to the growing field of artistic research that exists within and between these areas
Arrangements as a creative tool towards the performance of J. S. Bachβs Six Sonatas and Partitas for Solo Violin BWV 1001β1006
A performerβs musical interpretation reflects that performerβs creative discourse and praxis, which is often shaped by the performerβs exposure to concerts, recordings and pedagogical experiences. As a performance practice project, this dissertation proposes a way to expand a performerβs creative sources beyond these means by using arrangements as a creative tool. It models a process of score study that leads a performer to musical interpretations that are new to that performer.
The author is a violinist and focuses the study on J. S. Bachβs Six Sonatas and Partitas for Solo Violin, BWV 1001β1006 (hereafter the Solos), dated 1720. Three case studies demonstrate the use of arrangements as a creative tool, studying arrangements for harpsichord, organ, lute, piano and piano accompaniment drawn from the entire time span from Bachβs time to today. Each case study comprises detailed score studies of various passages in the Solos, each leading to musical interpretations that are new to the author.
This dissertationβs contribution to knowledge is the process rather than the authorβs particular outcomes. As every violinist is different, every violinist will find different aspects of these arrangements relevant and interesting, resulting in different interpretive findings. This is not only recognised but celebrated, as it makes the world of musical possibilities all the richer
Theorizing Music Perception and Cognition through Visualization of Sonic Phenomena and Mood Immersion
This thesis offers an in-depth examination of the VisualEars Project which exploresΒ music perception and cognition, as well as the experience of mood immersion through visualization of music as a sonic, gestural phenomenon by visual artists and aΒ consequentΒ immersive experience of simultaneous music-listening and art-observing by a widespread, virtual audience.Β The methodologies include ethnographic research, musical and visual analysis, self-reflexive observation, and a randomized and controlled PANAS scale to measure mood in a group. Based upon the general theories of perception such as gestalt and metaphoric understanding in musical and visual perception as well as the longstanding benefits of the arts on wellbeing, this thesis highlights cross-cultural commonalities in musical perception and demonstrates the positive impact of the exhibition on the audienceβs mood
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