Algorithmic composition (AC) and music information retrieval (MIR) can benefit each other. By compositional algorithms, scientists generate vast materials for MIR experiment; through MIR tools, composers instantly analyze abundant pieces to comprehend gross aspects. Although there are manifold musicologically valid MIR features, most of them are merely applicable to Western music. Besides, most high-level and low-level features are not interchangeable to retrieve from both symbolic and audio samples. We investigate the susceptibility of melodic pitch contour, a parameter from an AC model. It was created to regulate a generative monophonic melody's sensitivity to make a return after consecutive pitch intervals. It takes audio frequency values rather than symbolic pitch numbers into consideration. Hence we expect its intercultural and cross-level capabilities. To validate, we modify the original model from compositional to analytical functions. Our experimental results unveil a clear trend of mean susceptibilities from vocal to instrumental styles in 16522 samples from 81 datasets across numerous composers, genres, eras, and regions. We demonstrate the mutual benefits between AC and MIR. The parameter operates as an intercultural and crosslevel feature. The relationship between susceptibility and register width is surprising in several comparisons. Further investigation is ongoing to answer more questions.