NSF Award
0637918
This Small Business Innovation Research (SBIR) Phase I project aims at solving the computational problem of personalizing music search and recommendation. The recent explosion of digital music has created an urgent need for powerful knowledge management techniques and tools. Because of the highly subjective nature of musical content and perception, the best possible search strategy would rank media in a personalized fashion, based on each individual's tastes and preferences, from combined cultural and acoustic descriptions. The Echo Nest's predictive personalization technology computes and collects, collaboratively and automatically, cultural opinions online and acoustic content using unsupervised data mining and machine listening techniques. Combining cultural and acoustic notions of music together with the analysis of an individual's listening patterns, ratings and feedback, leads to a vertical search/recommendation engine that knows about content, communities' reaction, and users' preferences. <br/><br/>Intelligent music personalization goes beyond search and recommendation. Because the approach is fully autonomous and scalable it can efficiently address the long tail of independent music as well as the Billboard 100; discover artists and niches or predict trends and hits; market indies directly to individuals and optimize aggregators, distributors, and record labels' selection. The Echo Nest engine is the perceptual-media complement to purely text-based search engines and has a significant market potential.
