Vibrato is one of the most frequently investigated parameters in singing. Most studies examine a very limited number of sound samples to answer specific questions. In the novel approach of our interdisciplinary vibrato project, a comprehensive database of 1723 sound samples was compiled, containing examples from 30 operas, one oratorio and 73 vocal exercises. Single tones undisturbed by orchestral sound were selected in the vocal registers soprano, tenor, baritone and bass according to a standardized classification system dividing voices into either a lyric or dramatic voice type. Various algorithms were implemented in a Matlab environment to automatically evaluate sound samples, with respect to vibrato extent and rate. An AM/FM demodulation scheme is presented with postprocessing required for automatic calculation of these two parameters. The vibrato extent was higher for the dramatic compared to the lyric voice type in soprano and tenor voices. In contrast, the extent of lyric baritones was significantly higher than for dramatic ones. Moreover, the vibrato rate was slightly lower for the dramatic voice type in all vocal registers. It turned out that the classification of bass voices into lyric and dramatic, unlike the other vocal registers, was not scientifically reproducible with our methods. Further vibrato results regarding aging, intra-individual and general stylistic development over time are presented and their possible relevance for vocal pedagogy are discussed.

Public bookcases offer the opportunity to serendipitously discover books and to anonymously share books with others. The set of available books as well as the sharing patterns are highly dynamic, as anybody can freely take or donate books. This makes it difficult for users to see what is available or of interest to them. To support book sharing via public bookcases we developed a mobile AR application that highlights relevant books in the camera viewfinder and that facilitates searching for specific books. The application recognizes books via text and color features on the spine. In a lab study with 15 participants we evaluated our book recognition algorithm and found that it outperforms unaided visual search. We interviewed users of public bookcases and analyzed the bookcases’ setup and rate of change. A subsequent field evaluation of the AR application on nine public bookcases found a recognition accuracy of 80 % for 450 books under different conditions. The proposed approach provides the basis for effectively sharing books via public bookcases.