Zobrazeno 1 - 10
of 154
pro vyhledávání: '"music signal processing"'
Publikováno v:
Emitter: International Journal of Engineering Technology, Vol 11, Iss 2 (2023)
A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are th
Externí odkaz:
https://doaj.org/article/6f763f2177f046d8881e0eed185b479b
Autor:
Lazaros Moysis, Lazaros Alexios Iliadis, Sotirios P. Sotiroudis, Achilles D. Boursianis, Maria S. Papadopoulou, Konstantinos-Iraklis D. Kokkinidis, Christos Volos, Panagiotis Sarigiannidis, Spiridon Nikolaidis, Sotirios K. Goudos
Publikováno v:
IEEE Access, Vol 11, Pp 17031-17052 (2023)
The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music
Externí odkaz:
https://doaj.org/article/8bc73aef381c4d24923028c584c69d8c
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2021, Iss 1, Pp 1-17 (2021)
Abstract If music is the language of the universe, musical note onsets may be the syllables for this language. Not only do note onsets define the temporal pattern of a musical piece, but their time-frequency characteristics also contain rich informat
Externí odkaz:
https://doaj.org/article/145933a49ad94c8a8c9e7ae441380d02
Autor:
Takayuki Nakatsuka, Kento Watanabe, Yuki Koyama, Masahiro Hamasaki, Masataka Goto, Shigeo Morishima
Publikováno v:
IEEE Access, Vol 9, Pp 101994-102003 (2021)
We propose a learning-based method of estimating the compatibility between vocal and accompaniment audio tracks, i.e., how well they go with each other when played simultaneously. This task is challenging because it is difficult to formulate hand-cra
Externí odkaz:
https://doaj.org/article/2a5457fc8bf845a89e22beb25c6afb9c
Autor:
Albin Correya, Jorge Marcos-Fernández, Luis Joglar-Ongay, Pablo Alonso-Jiménez, Xavier Serra, Dmitry Bogdanov
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 4, Iss 1 (2021)
Open-source software libraries have a significant impact on the development of Audio Signal Processing and Music Information Retrieval (MIR) systems. Despite the abundance of such tools, there is a lack of an extensive and easy-to-use reference libra
Externí odkaz:
https://doaj.org/article/4472e2158afa4fb9949e03fe85bb80ad
Akademický článek
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Akademický článek
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Autor:
Oriol Nieto, Gautham J. Mysore, Cheng-i Wang, Jordan B. L. Smith, Jan Schlüter, Thomas Grill, Brian McFee
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 3, Iss 1 (2020)
With recent advances in the field of music informatics, approaches to audio-based music structural analysis have matured considerably, allowing researchers to reassess the challenges posed by the task, and reimagine potential applications. We review
Externí odkaz:
https://doaj.org/article/8db40efd30c245288c9ee9a0587f41e4
Publikováno v:
Transactions of the International Society for Music Information Retrieval, Vol 2, Iss 1 (2019)
The employment of playing techniques such as string bend and vibrato in electric guitar performance makes it difficult to transcribe the note events using general note tracking methods. These methods analyze the contour of fundamental frequency compu
Externí odkaz:
https://doaj.org/article/2fe2a63306c84afeb13d2745b0af3e91
Autor:
Masataka Goto, Masahiro Hamasaki, Kento Watanabe, Shigeo Morishima, Takayuki Nakatsuka, Yuki Koyama
Publikováno v:
IEEE Access, Vol 9, Pp 101994-102003 (2021)
We propose a learning-based method of estimating the compatibility between vocal and accompaniment audio tracks, i.e., how well they go with each other when played simultaneously. This task is challenging because it is difficult to formulate hand-cra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::925e79e19a359c678adab5e39951f1b1
https://ieeexplore.ieee.org/document/9481947/
https://ieeexplore.ieee.org/document/9481947/