Evaluation and modelling of perceived audio quality in popular music, towards intelligent music production

Abstract

This thesis addresses three fundamental questions: What is mixing? What makes a high-quality mix? How can high-quality mixes be automatically generated? While these may seem essential to the very foundations of intelligent music production, this thesis argues that they have not been sufficiently addressed in previous studies. An important contribution is the questioning of previously-held definitions of a 'mix'. Experiments were conducted in which participants used traditional mixing interfaces to create mixes using gain, panning and equalisation. The data was analysed in a novel 'mix-space', 'panning-space' and 'tone-space' in order to determine if there is a consensus in how these tools are used. Methods were developed to create mixes by populating the mix-space according to parametric models. These mixes were characterised by signal features, the distributions of which suggest tolerance bounds for automated mixing systems. This was complemented by a study of real-world music mixes, containing hundreds of mixes each for ten songs, collected from on-line communities. Mixes were shown to vary along four dimensions: loudness/dynamics, brightness, bass and stereo width. The variations between individual mix engineers were also studied, indicating a small effect of the mix engineer on mix preference ratings (eta2 = 0.021). Perceptual audio evaluation revealed that listeners appreciate 'quality' in a variety of ways, depending on the circumstances. In commercially-released music, 'quality' was related to the loudness/dynamic dimension. In mixes, 'quality' is highly correlated with 'preference'. To create mixes which maximised perceived quality, a novel semi-automatic mixing system was developed using evolutionary computation, wherein a population of mixes, generated in the mix-space, is guided by the subjective evaluations of the listener. This system was evaluated by a panel of users, who used it to create their ideal mixes, rather than the technically-correct mixes which previous systems strived for. It is hoped that this thesis encourages the community to pursue subjectively motivated methods when designing systems for music-mixing