In music production, descriptive terminology is used to define perceived sound transformations. By understanding the underlying statistical features associated with these descriptions, we can aid the retrieval of contextually relevant processing parameters using natural language, and create intelligent systems capable of assisting in audio engineering. In this study, we present an analysis of a dataset containing descriptive terms gathered using a series of processing modules, embedded within a Digital Audio Workstation. By applying hierarchical clustering to the audio feature space, we show that similarity in term representations exists within and between transformation classes. Furthermore, the organisation of terms in low-dimensional timbre space can be explained using perceptual concepts such as size and dissonance. We conclude by performing Latent Semantic Indexing to show that similar groupings exist based on term frequency.
We present an intelligent approach to multitrack dynamic range compression where all parameters are configured automatically based on side-chain feature extraction from the input signals. A method of adjustment experiment to explore how audio engineers set the ratio and threshold is described. We use multiple linear regression to model the relationship between different features and the experimental results. Parameter automations incorporate control assumptions based on this experiment and those derived from mixing literature and analysis. Subjective evaluation of the intelligent system is provided in the form of a multiple stimulus listening test where the system is compared against a no-compression mix, two human mixes, and an alternative approach. Results showed that mixes devised by our system are able to compete with or outperform manual mixes by semi-professionals under a variety of subjective criteria.
Artificial reverberation is an important music production tool with a strong but poorly understood perceptual impact. A literature review of the relevant works concerned with the perception of musical reverberation is provided, and the use of artificial reverberation in multisource mixes is studied. The perceived amount of total artificial reverberation in a mixture is predicted using relative reverb loudness and early decay time, as extracted from the newly proposed Equivalent Impulse Response. Results indicate that both features have a significant impact on the perception of a mix and that they are closely related to the upper and lower bounds of desired amount of reverberation in a mixture.
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