Adaptive signal processing
A Bayesian Model for Prelaunch Sales Forecasting of Recorded Music
Management Science
Content-based music audio recommendation
Proceedings of the 13th annual ACM international conference on Multimedia
Mapping the mental space of game genres
Proceedings of the 2007 ACM SIGGRAPH symposium on Video games
Social Knowledge-Driven Music Hit Prediction
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Musical taste is highly individualized and evolves over time in seemingly unpredictable ways. However popular trends emerge as collective and potentially predictable patterns of preference for genre and style. The goal of this paper is to reveal these patterns and extrapolate how a statistically 'average' populace responds to new stimuli.This paper addresses three questions. One is to find statistically meaningful patterns within the data. The next question is if we can predict how long an album will stay in chart, given the first few weeks' sales data, using statistical patterns found from the first question. The last question is to see if a new album's position in chart can be predicted on a certain week in the future (such as the 5th week or 12th week), with the first few weeks' sales data. For this, we used LMS (least mean square) algorithm, a well known adaptive algorithm.This paper uses published bi-weekly sales data from the Billboard magazine, more specifically, the Top Jazz chart. The results show some interesting correlations, one of which emphasizes the role of marketing. According to our findings, it is probably worth a good investment on marketing before starting sales of an album, since the data shows that the higher the starting position of an album is, the longer it is likely to stay in chart.