@inproceedings{621aeadc3e474f82a4a79a860e8e1824,
title = "Automatic multi-track mixing by kernel dependency estimation",
abstract = "Due to the revolution of digital music, people can create recordings in a home studio with cheaper gear. However multi-track recordings need to be mixed to combine them into one or more channels. The question is that mixing requires background knowledge in sound engineering and psychoacoustics. It is difficult to get good mixdown for non-specialist in sound engineer. In this paper, we use supervised learning method for automatically mixing multi-track recording into coherent and well-balanced piece. Due to lack of mixing parameters, first we estimate the weight of mixing parameters by using the relation between raw multi-track and mixdown. Given the mixing parameters for any music genre, we use kernel decency estimation method to create our mixing model. The experiment show KDE is 42 able to make a more satisfactory estimation than treating each parameter independently.",
keywords = "Kernel dependency estimation, Mixing, Music IR, Music production",
author = "Wu, {Tsung Ting} and Chang, {Chia Hui}",
note = "Publisher Copyright: Copyright {\textcopyright} 2014 the Association for Computational Linguistics and Chinese Language Processing (ACLCLP).; 26th Conference on Computational Linguistics and Speech Processing, ROCLING 2014 ; Conference date: 25-09-2014 Through 26-09-2014",
year = "2014",
month = sep,
day = "1",
language = "???core.languages.en_GB???",
series = "Proceedings of the 26th Conference on Computational Linguistics and Speech Processing, ROCLING 2014",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "42--57",
editor = "Chia-Hui Chang and Hsin-Min Wang and Jen-Tzung Chien and Hung-Yu Kao and Shih-Hung Wu",
booktitle = "Proceedings of the 26th Conference on Computational Linguistics and Speech Processing, ROCLING 2014",
}