In this paper, we present a model for the modulation of multiperformer microtiming variation in musical groups. This is done using a multivariate Markov model, in which the relationship between players is modelled using an interdependence matrix (α) and a multidimensional state transition matrix (S). This method allows us to generate more natural sounding musical sequences due to the reduction of out-of-phase errors that occur in Gaussian pseudorandom and player-independent probabilistic models. We verify this using subjective listening tests, where we demonstrate that our multivariate model is able to outperform commonly used univariate models at producing human-like microtiming variability. Whilst the participants in our study judged the real time sequences performed by humans to be more natural than the proposed model, we were still able to achieve a mean score of 63.39% naturalness, suggesting microtiming interdependence between players captured in our model significantly enhances the humanisation of group musical sequences.

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We present a method for mapping between the input space of a parametric equaliser and a lower-dimensional representation, whilst preserving the effect’s dependency on the incoming audio signal. The model consists of a parameter weighting stage in which the parameters are scaled to spectral features of the audio signal, followed by a mapping process, in which the equaliser’s 13 inputs are converted to (x, y) coordinates. The model is trained with parameter space data representing two timbral adjectives (warm and bright), measured across a range of musical instrument samples, allowing users to impose a semantically-meaningful timbral modification using the lower-dimensional interface. We test 10 mapping techniques, comprising of dimensionality reduction and reconstruction methods, and show that a stacked autoencoder algorithm exhibits the lowest parameter reconstruction variance, thus providing an accurate map between the input and output space. We demonstrate that the model provides an intuitive method for controlling the audio effect’s parameter space, whilst accurately reconstructing the trajectories of each parameter and adapting to the incoming audio spectrum.

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In sound production, engineers cascade processing modules at various points in a mix to apply audio effects to channels and busses. Previous studies have investigated the automation of parameter settings based on external semantic cues. In this study, we provide an analysis of the ways in which participants apply full processing chains to musical audio. We identify trends in audio effect usage as a function of instrument type and descriptive terms, and show that processing chain usage acts as an effective way of organising timbral adjectives in low-dimensional space. Finally, we present a model for full processing chain recommendation using a Markov Chain and show that the system’s outputs are highly correlated with a dataset of user-generated processing chains.

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In musical timbre, two of the most commonly used perceptual dimensions are warmth and brightness. In this study, we develop a model capable of accurately controlling the warmth and brightness of an audio source using a single parameter. To do this, we first identify the most salient audio features associated with the chosen descriptors by applying dimensionality reduction to a dataset of annotated timbral transformations. Here, strong positive correlations are found between the centroid of various spectral representations and the most salient principal components. From this, we build a system designed to manipulate the audio features directly using a combination of linear and nonlinear processing modules. To validate the model, we conduct a series of subjective listening tests, and show that up to 80% of participants are able to allocate the correct term, or synonyms thereof, to a set of processed audio samples. Objectively, we show low Mahalanobis distances between the processed samples and clusters of the same timbral adjective in the low-dimensional timbre space.

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