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— In this paper we describe a novel approach to applying text-based information retrieval techniques to music collections. We represent tracks with a joint vocabulary consisting of both conventional words, drawn from social tags, and audio muswords, representing characteristics of automatically-identified regions of interest within the signal. We build(More)
In modern societies, cultural change seems ceaseless. The flux of fashion is especially obvious for popular music. While much has been written about the origin and evolution of pop, most claims about its history are anecdotal rather than scientific in nature. To rectify this, we investigate the US Billboard Hot 100 between 1960 and 2010. Using music(More)
A method for segmenting musical audio with a hierarchical timbre model is introduced. New evidence is presented to show that music segmentation can be recast as clustering of timbre features, and a new clustering algorithm is described. A prototype thumbnail-generating application is described and evaluated. Experimental results are given, including(More)
Using a dataset of 7 billion recent submissions to the Last.fm Scrobble API 1 , we investigate possible popularity bias in Last.fm's recommendations and streaming radio services. In particular we compare the recent listening of users who listen regularly to Last.fm streaming services with those who listen less often or never. Finally we describe a new(More)
We propose the novel audio feature structural change for the analysis and visualisation of recorded music, and argue that it is related to a particular notion of musical complexity. Structural change is a meta feature that can be calculated from an arbitrary frame-wise basis feature, with each element in the structural change feature vector representing the(More)