# Assessment of Student Music Performances Using Deep Neural Networks
## 2 Related Work  
Music is more than the written score. While the score may capture the composer's intent and provides a \"blueprint for a performance\" [29], only an acoustic rendition by a performer interpreting the score can allow for a complete perception of music. Different performances of the same score can be drastically different. This is primarily because the performer has to interpret, modify, or enhance the available score information and devise a strategy to convey the score to the listener by means of choosing and varying, for example, tempo and timing, dynamics, intonation and ornamentation, and tone quality. This leads to a unique and \"expressive\" music performance [29]. Music performance analysis (MPA) is a research area aimed at measuring, understanding, and modeling the impact of such expressive alterations on the human listener [30]. While most of the early research in MPA centered around analyzing symbolic data extracted from piano rolls or musical instrument digital interface (MIDI) devices (compare, e.g., [31; 32]), attention has recently shifted to the analysis of audio signals (compare, e.g., [30; 33]). The idea of utilizing technology to assist music (performance) education is not new. [PERSON] pointed out the value of scientific observation of music performances for improving learning as early as the 1930s [34]. One of the earliest works exploring the potential of computer-assisted techniques in the music classroom was carried out by [PERSON] [35]. Although [PERSON]'s work was focused on using technology for providing individualized instruction and developing aural and visual aids to facilitate learning, it also highlighted the potential of using MIR techniques such as pitch detection to perform error analysis in a musical performance and provide constructive feedback to the learners. The majority of the attempts at creating automatic music assessment systems so far have relied on extracting standard and hand-crafted features from the audio signal and then feeding them to a classifier to characterize the quality of the performance. [PERSON] et al., for example, used a set of standard audio features and a support vector machine (SVM) classifier to categorize tonal quality of trumpet performances into \"good\" or \"bad\" labels [12]. [PERSON] et al., on the other hand, identified pitch interval accuracy and vibrato as important criteria for the evaluation of singing voice and designed hand-crafted features to quantify them [11]. [PERSON] et al. used expert interviews to identify metrics for a \"good\" sound; based on these interviews, they designed custom features to measure pitch and timbre stability to evaluate the quality of an instrumental performance [15]. [PERSON] et al. characterized the quality of student vocal and instrumental performances using features measuring the pitch, intonation, and rhythmic accuracy [14]. [PERSON] et al. used a combination of spectral, timbral and pitch based features to identify common mistakes made by beginner violin players [16]. [PERSON] et al. tried modeling expressiveness in violin performance using their own set of features intended to capture dynamics, duration, and vibrato [17]. [PERSON] et al. proposed using features based on audio-to-score alignment to compute a distance metric between the performance and the score being performed [19]. These were then combined with other features extracted from pitch, amplitude, and inter-onset intervals, and were used to build regression models to predict ratings given by expert judges along several performance criteria such as musicality, note accuracy, rhythmic accuracy and tone quality. Features based on aligned pitch contours--although in this case to a reference performance--were also used by [PERSON] et al. to analyze vocal conservatory exam recordings and classify them as \"pass\" or \"fail\" [20].