Leveraging Computer Vision Networks for Guitar Tablature Transcription

Generating music-related notations offers assistance for musicians in the path of replicating the music using a specific instrument. In this paper, we evaluate the state-of-the-art guitar tablature transcription network named TabCNN against state-of-the-art computer vision networks. The evaluation is performed using the same dataset as well as the same evaluation metrics of TabCNN. Furthermore, we propose a new CNN-based network named TabInception to transcribe guitar-related notations, also called guitar tablatures. The network relies on a custom inception block converged by dense layers. The TabInception network outperforms the TabCNN in terms of multi-pitch precision (MP), tablature precision (TP), and tablature F-measure (TF). Moreover, the Swin Transformer achieves the best score in terms of multi-pitch recall (MR) and tablature recall (TR), while the Vision Transformer achieves the best score in terms of multi-pitch F-measure (MF). Motivated by the previous insights, we train the networks with more epochs and propose another network named Inception Transformer (InT) to surpass all the estimation metrics of TabCNN using a single network. The InT network relies on an inception block converged by a Transformer Encoder. The TabInception and the InT network outperformed all estimation metrics of TabCNN except the tablature disambiguation rate (TDR) when trained using a bigger epoch size.