Data-to-music sonification and user engagement.

The process of transforming data into sounds for auditory display provides unique user experiences and new perspectives for analyzing and interpreting data. A research study for data transformation to sounds based on musical elements, called data-to-music sonification, reveals how musical characteristics can serve analytical purposes with enhanced user engagement. An existing user engagement scale has been applied to measure engagement levels in three conditions within melodic, rhythmic, and chordal contexts. This article reports findings from a user engagement study with musical traits and states the benefits and challenges of using musical characteristics in sonifications. The results can guide the design of future sonifications of multivariable data.

The sound of migration: exploring data sonification as a means of interpreting multivariate salmon movement datasets.

The migration of Pacific salmon is an important part of functioning freshwater ecosystems, but as populations have decreased and ecological conditions have changed, so have migration patterns. Understanding how the environment, and human impacts, change salmon migration behavior requires observing migration at small temporal and spatial scales across large geographic areas. Studying these detailed fish movements is particularly important for one threatened population of Chinook salmon in the Snake River of Idaho whose juvenile behavior may be rapidly evolving in response to dams and anthropogenic impacts. However, exploring movement data sets of large numbers of salmon can present challenges due to the difficulty of visualizing the multivariate, time-series datasets. Previous research indicates that sonification, representing data using sound, has the potential to enhance exploration of multivariate, time-series datasets. We developed sonifications of individual fish movements using a large dataset of salmon otolith microchemistry from Snake River Fall Chinook salmon. Otoliths, a balance and hearing organ in fish, provide a detailed chemical record of fish movements recorded in the tree-like rings they deposit each day the fish is alive. This data represents a scalable, multivariate dataset of salmon movement ideal for sonification. We tested independent listener responses to validate the effectiveness of the sonification tool and mapping methods. The sonifications were presented in a survey to untrained listeners to identify salmon movements with increasingly more fish, with and without visualizations. Our results showed that untrained listeners were most sensitive to transitions mapped to pitch and timbre. Accuracy results were non-intuitive; in aggregate, respondents clearly identified important transitions, but individual accuracy was low. This aggregate effect has potential implications for the use of sonification in the context of crowd-sourced data exploration. The addition of more fish, and visuals, to the sonification increased response time in identifying transitions.

Melody discrimination and protein fold classification.

One of the greatest challenges in theoretical biophysics and bioinformatics is the identification of protein folds from sequence data. This can be regarded as a pattern recognition problem. In this paper we report the use of a melody generation software where the inputs are derived from calculations of evolutionary information, secondary structure, flexibility, hydropathy and solvent accessibility from multiple sequence alignment data. The melodies so generated are derived from the sequence, and by inference, of the fold, in ways that give each fold a sound representation that may facilitate analysis, recognition, or comparison with other sequences.