• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.41-50
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• 2021

While artificial intelligence is rapidly replacing human jobs, the art field where human creativity is important is considered an exception. There are currently several AI mastering services in the field of mastering music, a profession at the border between art and technology. In general, the quality of AI mastering is considered to be inferior to the work of a human professional mastering engineer. In this paper, acoustic analysis, listening experiments, and expert interviews were conducted to compare AI and human experts. In the acoustic analysis, In the analysis of audio, there was no significant difference between the results of professional mastering engineers and the results of artificial intelligence. In the listening experiment, the non-musicians could not distinguish between the sound quality of the professional mastering engineer's work and the artificial intelligence work. The group of musicians showed a preference for a specific sound source, but the preference for a specific mastering did not appear significantly. In an expert interview, In expert interviews, respondents answered that there was no significant difference in quality between the two mastering services, and the biggest difference was the communication method between the mastering service provider and the user. In addition, as data increases, it is expected that artificial intelligence mastering will achieve rapid quality improvement and further improvement in communication.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.826-836
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• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• Proceedings of the KAIS Fall Conference
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• 2010.05b
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• pp.599-602
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• 2010

음악 제작 과정의 마지막 단계는 마스터링이다. 과거의 마스터링은 단순히 컴프레서(Compressor)와 리미터(Limiter)를 이용하여 각 트랙의 오디오 레벨을 일률적으로 매칭시키는 것이었지만, 디지털 장비의 꾸준한 발전으로 많은 마스터링 제작자들은 이퀄라이저(Equalizer)와 컴프레서를 기본으로 리버브(Reverb), 딜레이(Delay), 그리고 디더(Dither)를 이용하여 모든 트랙을 하나의 통일된 분위기로 제작하는 방식으로 진보하고 있다.