Sinusoidal modeling is one of the most common techniques for general purpose audio synthesis and analysis. Owing to the ever increasing amount of available computational resources, nowadays practically all types of sounds can be constructed up to a certain degree of perceptual accuracy. However, the method is computationally expensive and can for some cases, particularly for transient signals, still exceed the available computational resources. In this work methods derived from the realm of machine learning are exploited to provide a simple and efficient means to estimate the achievable reconstruction quality. The peculiarities of common classes of musical instruments are discussed and finally, the existing metrics are extended by information on the signal's phase propagation to allow for more accurate estimations.
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