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arxiv: 2412.03373 · v1 · pith:4V2D7QCV · submitted 2024-12-04 · cs.SD · eess.AS

Exploring trends in audio mixes and masters: Insights from a dataset analysis

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classification cs.SD eess.AS
keywords audioanalysisissuesmasteredmastersmixesresultscompression
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We present an analysis of a dataset of audio metrics and aesthetic considerations about mixes and masters provided by the web platform MixCheck studio. The platform is designed for educational purposes, primarily targeting amateur music producers, and aimed at analysing their recordings prior to them being released. The analysis focuses on the following data points: integrated loudness, mono compatibility, presence of clipping and phase issues, compression and tonal profile across 30 user-specified genres. Both mixed (mixes) and mastered audio (masters) are included in the analysis, where mixes refer to the initial combination and balance of individual tracks, and masters refer to the final refined version optimized for distribution. Results show that loudness-related issues along with dynamics issues are the most prevalent, particularly in mastered audio. However mastered audio presents better results in compression than just mixed audio. Additionally, results show that mastered audio has a lower percentage of stereo field and phase issues.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering

    cs.SD 2025-08 unverdicted novelty 6.0

    SonicMaster is a text-conditioned flow-matching generative model for unified music restoration and mastering, trained on a dataset of simulated degradations across equalization, dynamics, reverb, amplitude, and stereo.