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Mixboard is a web / iOS application that allows music lovers to create and share personalized musical mashups. The app allows users to choose and organize up to four songs within four different lanes. The system automatically separates the songs' sources into corresponding stems, calculates an appropriate tempo and key for the mashup, and chooses song segments according to users' visual creation. Unlike other professional applications used for mashups, Mixboard does not require experience with Digital Audio Workstations (DAWs) or waveform editing and supports unlimited library of usable songs. In a co-creative fashion, users can explore their creativity while the system contributes its own creative input utilizing Music Information Retrieval (MIR), Digital Signal Processing (DSP), and compositional templates. User studies were conducted to evaluate Mixboard's success in achieving an effective balance between system automation and user control. Results indicate strong metrics for user creative expression, engagement, and ownership, as well as high satisfaction with the final musical outcome. Results also suggest a number of modifications to the balance between user control and system automation, which will be addressed in future work.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |