Perceptual Mixing for Musical Production

Abstract

A general model of music mixing is developed, which enables a mix to be evaluated as a set of acoustic signals. A second model describes the mixing process as an optimisation problem, in which the errors are evaluated by comparing sound features of a mix with those of a reference mix, and the parameters are the controls on the mixing console. Initial focus is placed on live mixing, where the practical issues of: live acoustic sources, multiple listeners, and acoustic feedback, increase the technical burden on the mixing engineer. Using the two models, a system is demonstrated that takes as input reference mixes, and automatically sets the controls on the mixing console to recreate their objective, acoustic sound features for all listeners, taking into account the practical issues outlined above. This reduces the complexity of mixing live music to that of recorded music, and unifies future mixing research. Sound features evaluated from audio signals are shown to be unsuitable for describing a mix, because they do not incorporate the effects of listening conditions, or masking interactions between sounds. Psychophysical test methods are employed to develop a new perceptual sound feature, termed the loudness balance, which is the first loudness feature to be validated for musical sounds. A novel, perceptual mixing system is designed, which allows users to directly control the loudness balance of the sounds they are mixing, for both live and recorded music, and which can be extended to incorporate other perceptual features. The perceptual mixer is also employed as an analytical tool, to allow direct measurement of mixing best practice, to provide fully-automatic mixing functionality, and is shown to be an improvement over current heuristic models. Based on the conclusions of the work, a framework for future automatic mixing is provided, centred on perceptual sound features that are validated using psychophysical methods