# Assessment of Student Music Performances Using Deep Neural Networks
## 6 Conclusions
Examples include the loss of timbral and dynamic information during the pitch extraction process or the loss of the phase in a magnitude spectrum. Therefore, learning from raw audio--though challenging--presents opportunities to not only improve the performance of the models at this task, but also further our understanding of music perception in general. 2. _Adding the musical score_: Although the human judges have access to the score that the students are performing, the DNN models do not. It would be of interest to design a model that allows encoding the score information in the input representation. 3. _Improving the training data_: While we use the concept of weak labels to improve model training by increasing the amount of data available, other more popular data augmentation methods such as pitch shifting can be used to potentially increase the models' robustness. Adding performances from other instruments to increase the available data is also an option, but requires adequate care to avoid bias towards a particular class of instruments. Given enough data, an alternative option would be to train instrument specific models, which can then capture the nuances and assessment criteria of the individual instruments better. 4. _Modifying the training methodology_: Since the models are trained on one dataset with a limited number of instruments, they cannot be directly used on other music performance datasets. However, based on our preliminary cross-category analysis results, transfer learning [71] could be an option for certain assessment criteria and warrants further investigation. 5. _Investigating alternative model analysis techniques_: Along with the techniques used in this article, other model analysis techniques such as layer-wise relevance propagation [70] can be explored to further improve our understanding of the feature learning process. Improving the interpretability of DNNs is an active area of research in the Artificial Intelligence community and more efforts are needed to build better tools targeting music and general audio-based tasks. 6. _Combining multiple modalities_: A music performance mostly caters to the auditory sense. However, visual cues such as gestures form a significant component of a music performance, which might influence the perception of expressiveness [72; 73]. Using multi-modal input representations, which encode both auditory and visual information can provide several interesting possibilities for future research. In this study, we show that DNNs are a promising tool for building computational models that can enable researchers to explore the area of music performance analysis in new and interesting ways. While considerable research still needs to be done before we can develop a comprehensive understanding of various aspects of a musical performance, our ability to interpret the models and to learn from feature representations will continue to be a critical factor in this endeavor. The authors would like to thank the Florida Bandmasters Association for providing the dataset used in this study. We also gratefully acknowledge NVIDIA Corporation (Santa Clara, CA, United States) who supported this research by providing a Titan X GPU via the NVIDIA GPU Grant program. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] implemented the models, performed the experiments, and analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest.