By Valeri Tsatsishvili
Автор, основываясь на строго научном подходе, рассматривает вопросы, возникающие при попытке классифицировать жанры метал-музыки и предлагает собственное решение проблемы. Предлагаемый материал - часть его магистерской диссертации.
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Additional info for Automatic Subgenre Classification of Heavy Metal Music
This approach became a standard feature of the genre in the mid 1990s. Symphonic elements practiced by Paradise Lost were adopted by the next generation of the bands such as Tristania, Theatre of Tragedy which founded symphonic gothic branch. Nowadays, the active members of symphonic gothic scene tend to bias towards more polished, commercially oriented music, while almost completely removing growling vocals and other extreme metal influences. 18 NU-Metal Nu-Metal is another fusion genre combining the elements of relatively modern popular genres outside of metal such as electronic music, hip hop, post punk, grunge and funk.
24 AUTOMATIC SUBGENRE CLASSIFICATION OF HEAVY METAL MUSIC Vocals are clean and melancholic vocals. Lyrics are commonly about depression, grief and fear, but also cover such themes as mythology, fantasy or battle. The latter is often referred to as epic doom. 9 Doom-Death Metal Doom-Death Metal is practically an extinct subgenre which emerged in the early 1990s and defined acts such as Paradise Lost, My Dying Bride and Anathema. Music from their early albums blends traditional doom music, female vocals, but also deep growls, dissonant riffs, sections with relatively faster tempos borrowed from death metal.
Improving timbre similarity: How high is the sky? Journal of Negative Results in Speech and Audio Sciences (1(1)). , & Pachet, F. (2003). Representing Musical Genre: A State of the Art. Journal of New Music Research , 32, 83-93. Barbedo, J. , & Lopes, A. (2007). Automatic Genre Classification of Musical Signals. EURASIP Journal on Advances in Signal Processing , Vol. 2007, Article ID 64960, 112. , & Kegl, B. (2006). Aggregate features and AdaBoost for music classification. Machine Learning , 65, 473-484.