# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 7 Discussion  
In the previous sections, we proposed a modular approach to the dimensionality reduction pipeline as applied to interfaces for interacting with sound collection. We have presented a framework that generalizes previous approaches in terms of features and potential for combinations. In comparison with previous approaches using scatterplots of two scalar descriptors, dimensionality reduction can be used to automate the selection of the axes, freeing users (often musicians and sound designers) from having to decide on the basis of signal processing concepts represented by the descriptors. By introducing a configuration option for position features, we introduce this choice again, although in this context the decision would be mainly to focus either on tonality (using chroma) or timbre (using MFCCs or MPEG7 features), as opposed to picking scalar descriptors. We have introduced the idea of feature learning, which again can be used to automate the choice of features.  
In our experiments, we have found that the feature learning algorithm based on spherical k-means can adapt better to sounds with more noise and variability than instrument sounds, such as environmental sounds. However, we have not investigated whether it could be used for tonal material. We hope that future work will contribute towards further automation of the analysis process, with the aim of obtaining intuitive parameters and choices from data.  
We have also introduced more flexibility for mapping descriptors to color. Prior work, such as [6], has used data clustering on the 2D plot to provide colors. This could be seen as redundant, since the clusters are already visible in the plot without colors. However, in our experience, it is still important that the color is related to the spatial distribution. While a formal evaluation would be needed to characterize this problem, our experiences so far indicate that it is hard to make sense of plots where color is completely unrelated to position. Again this presents another promising direction for automating the feature selection and extraction process.  
We have proposed and evaluated the introduction of grid layouts through the Hungarian algorithm. We have shown that this has two separate advantages. On the one hand, it allows introducing more information about each sound through icons, as it avoids overlaps. On the other hand, the grid can be used to provide more compact layouts that make a more efficient use of screen real estate. As we have shown, oversampling the grid can be used to recover the clusterings of the plots obtained via dimensionality reduction, as reflected in the trustworthiness and classification error measures, while preserving the regularity of the layout that allows representing icons.  
With respect to the icons, our approach is deliberately simple. Recent efforts to assess the usefulness of sound icons in visualizations [21] have been inconclusive. In our experience, using simple waveform representations of well-known descriptors is encouraging. In this sense, the creation of meaningful icons from audio features is an interesting design problem by itself that deserves further research. It is worth mentioning that, in early work, we experimented with mapping color to the time series of audio features in the sound icons (as is done for example in the Freesound database). However as mentioned above, we found that when color is decoupled from position, the plot can be hard to interpret, so the combination of two scalar features for the waveform (derived from the time series) and a solid color (derived from the statistic of the time series) provided a good compromise. In early experiments with waveform icons, we also realized that, while it may be tempting to normalize the waveform locally to optimize the space allocated for the icon, global normalization was crucial to be able to understand the icons in the context of the plot. A formal user study would help confirm these findings.