In audio post-production, the adoption of sound synthesis offers a viable alternative for searching and recording samples in creating soundscapes. However, a central concern arises regarding the ability of synthetic sounds to match the perceived authenticity of library samples. This paper introduces an analytical approach, examining authentic and synthetic samples in five categories(burning embers, pouring water, explosions, popping bubbles and church bells) by delving into audio descriptors that distinguish both types. We focus in the utilization of machine learning classification models and a perceptual evaluation experiment. The perceptual evaluation was between five distinct synthesis techniques – granular, additive, subtractive, physically informed, and modal synthesis –revealed that subtractive synthesis is perceived as more realistic in explosion sounds, while additive synthesis works better with pouring water sounds. This study provides valuable insights into the audio descriptors that may require modification in specific synthetic models, paving the way for a deeper understanding of sound synthesis methods and facilitating their integration into the sound design process.
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