# DExter: Learning and Controlling Performance Expression with Diffusion Models
## 5 Evaluation
### Transferring from a Source Performance  
As suggested by [PERSON] et al. [42] and [PERSON] et al. [43], a style transfer can be achieved in a diffusion framework by using, as a starting point for generation, a shallowly noised version of the source information. Given the large amount of music overlap in our datasets, we could test this by forming data pairs that consisted of two interpretations of the same piece to be used as the source and target p_codec in this experiment.  
Given a source performance codec, \(x_{src}\), we calculated its noisy version, \(x_{t_{0}}\), with a predefined time step, \(t_{0}\leq T\), according to the forward process shown in Equation (1). By using \(x_{t_{0}}\) as the starting point for the reverse process of a pretrained model, we enabled the manipulation of performance \(x_{src}\) with a target mid-level condition and a shared score condition, \(c_{(s,tgt)}\), in a shallow reverse process, \(p_{g}(\delta_{0t_{0}}|x_{t_{0}},c_{(s,tgt)})\), as illustrated in Figure 1 (top right). With the transfer experiments, we attempted to understand the following two questions:  
_1. Does transferring help with the final generation quality compared with rendering from scratch?_  
In the transfer experiment, we combined pairs of ground-truth performances of the same piece segment, \(x_{src},x_{tgt}\), where \(s_{src}=s_{tgt}\). The same testing set as that used in the previous sections was used, and the source performance was randomly taken from the ground truth. We experimented with different transfer steps of \(t_{0}\in\{T,\frac{3T}{4},\frac{T}{2},\frac{T}{4}\}\), and we reported the global metrics of the tempo curve and velocity curve with their deviation and correlation.  
What we observed in Table 3 was that transferring from a source performance slightly helped with initialization. Specifically, employing a denoiser for three-quarters of the diffusion steps--ideally preserving around one-quarter of the source's characteristics--yielded the highest-quality outcomes. However, the transfer quality did not steadily  
Figure 3: Steering the expressive characteristics of generated performances using mid-level features (c_codec; see Section 4.1) for conditions. For each piece, a performance is generated with the c_codec derived from an actual performance of the piece, and two further performances are generated with one of the mid-level features doubled (2\(\times\)) or halved (0.5\(\times\)). The average difference between the halved and unmodified conditions, and between the doubled and unmodified conditions, areplotted here.  
improve with the retained information from the source: the \(\frac{T}{4}\)-step transfer resulted in ambiguous outputs that did not align well with the given score.  
_2. Does a transferred rendering sound 'closer' to the source or the target?_  
We wished, similar to Section 5.2, to measure the transfer proximity using the predicted perceptual features. The radar plots in Figure 4 show the seven perceptual feature dimensions predicted via the proxy, illustrating the perceptual distance between the source, target, and generated performance for three different transfer gradations, \(\frac{T}{4}\), \(\frac{T}{2}\), \(\frac{3T}{4}\). At \(\frac{T}{4}\) steps, the predicted performance deviated from both the source and target, which fit out previous observation that insufficient denoising steps result in ambiguous outputs. As the transfer step increases, there is a discernible shift in the predicted output towards the target profile across most perceptual dimensions.