Musical Mix Clarity Predication using Decomposition and Perceptual Masking Thresholds

TL;DR

This paper introduces a perceptual model that decomposes music signals into components, calculates masking thresholds, and predicts perceived mix clarity with high correlation to subjective scores, aiding music mixing and retrieval.

Contribution

The work presents a novel perceptual model for mix clarity based on signal decomposition and masking thresholds, validated against subjective ratings.

Findings

Best model variant achieved Spearman's rho = 0.8382

Masking thresholds correlate strongly with subjective clarity scores

Noise-like residuals negatively impact perceived mix clarity

Abstract

Objective measurement of perceptually motivated music attributes has application in both target driven mixing and mastering methodologies and music information retrieval. This work proposes a perceptual model of mix clarity which decomposes a mixed input signal into transient, steady-state, and residual components. Masking thresholds are calculated for each component and their relative relationship is used to determine an overall masking score as the model's output. Three variants of the model were tested against subjective mix clarity scores gathered from a controlled listening test. The best performing variant achieved a Spearman's rank correlation of rho = 0.8382 (p<0.01). Furthermore, the model output was analysed using an independent dataset generated by progressively applying degradation effects to the test stimuli. Analysis of the model suggested a close relationship between the proposed model and the subjective mix clarity scores particularly when masking was measured using linearly spaced analysis bands. Moreover, the presence of noise-like residual signals was shown to have a negative effect on the perceived mix clarity.