Analysis and prediction of the audio feature space when mixing raw recordings into individual stems
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Embargoed until: 5555-01-01
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Embargoed until: 5555-01-01
Reason: Version Not Permitted
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Processing individual stems from raw recordings is one of the first steps of multitrack audio mixing. In this work, we explore which set of low-level audio features are sufficient to design a prediction model for this transformation. We extract a large set of audio features from bass, guitar, vocal and keys raw recordings and stems. We show that a procedure based on random forests classifiers can lead us to reduce significantly the number of features and we use the selected audio features to train various multi-output regression models. Thus, we investigate stem processing as a content-based transformation, where the inherent content of raw recordings leads us to predict the change of feature values that occurred within the transformation.
Authors
Martinez Ramirez, M; Reiss, J; 143rd Audio Engineering Society ConventionCollections
- Physics and Astronomy [1328]