Automatic Music Transcription (AMT) entails creating an algorithm that converts an acoustic signal from an audio file into the corresponding sheet music representation. This paper uses deep learning methods and models AMT as a translation problem, comparing the effectiveness of an instance-based translation approach using an MLP to a sequence-based approach using an RNN. The models were trained on the EsAc dataset and evaluated using MUSTER metrics. The results show that the instance-based model better classifies the correct pitch. However, the sequence-based approach outperforms the instance-based approach on all other aspects of the MUSTER metrics, producing a 98% accuracy.