This paper investigates the impact of rhythm, tempo, and rest variations on pitch detection in deep learning-based models for piano transcription. We conducted a series of experiments using GRU and Transformer architectures, manipulating note lengths, rhythmic patterns, and rest intervals to assess their effect on pitch transcription accuracy. Our findings indicate that model performance is significantly influenced by these musical factors. The experiment with GRU shows notable sensitivity to rhythmic and rest changes. However, the Transformer model handles varied conditions more robustly. These findings help refine our approach to music transcription software, particularly in improving pitch recognition across varied rhythmic patterns, tempos and rests.
