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The control of interpretational properties such as duration, vibrato, and dynamics is important in music performance. Musicians continuously manipulate such properties to achieve different expressive intentions. This paper presents a synthesis system that automatically converts a mechanical, deadpan interpretation to distinct expressions by controlling these expressive factors. Extending from a prior work on expressive musical term analysis, we derive a subset of essential features as the control parameters, such as the relative time position of the energy peak in a note and the mean temporal length of the notes. An algorithm is proposed to manipulate the energy contour (i.e. for dynamics) of a note. The intended expressions of the synthesized sounds are evaluated in terms of the ability of the machine model developed in the prior work. Ten musical expressions such as Risoluto and Maestoso are considered, and the evaluation is done using held-out music pieces. Our evaluations show that it is easier for the machine to recognize the expressions of the synthetic version, comparing to those of the real recordings of an amateur student. While a listening test is under construction as a next step for further performance validation, this work represents to our best knowledge a first attempt to build and quantitatively evaluate a system for EMT analysis/synthesis.

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Removing audio effects from electric guitar recordings makes it easier for post-production and sound editing. An audio distortion recovery model not only improves the clarity of the guitar sounds but also opens up new opportunities for creative adjustments in mixing and mastering. While progress have been made in creating such models, previous efforts have largely focused on synthetic distortions that may be too simplistic to accurately capture the complexities seen in real-world recordings. In this paper, we tackle the task by using a dataset of guitar recordings rendered with commercial-grade audio effect VST plugins. Moreover, we introduce a novel two-stage methodology for audio distortion recovery. The idea is to firstly process the audio signal in the Mel-spectrogram domain in the first stage, and then use a neural vocoder to generate the pristine original guitar sound from the processed Mel-spectrogram in the second stage. We report a set of experiments demonstrating the effectiveness of our approach over existing methods, through both subjective and objective evaluation metrics.

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