As the first decade of the 21st century comes to a close, growth in multimedia delivery infrastructure and public demand for applications built on this backbone are converging like never before. The push towards reaching truly interactive multimedia technologies becomes stronger as our media consumption paradigms continue to change. In this paper, we profile a technology leading the way in this revolution: active learning. Active learning is a strategy that helps alleviate challenges inherent in multimedia information retrieval through user interaction. We show how active learning is ideally suited for the multimedia information retrieval problem by giving an overview of the paradigm and component technologies used with special attention given to the application scenarios in which these technologies are useful. Finally, we give insight into the future of this growing field and how it fits into the larger context of multimedia information retrieval.

Large music collections, ranging from thousands to millions of tracks, are unsuited to manual searching, motivating the development of automatic search methods. When different musicians perform the same underlying song or piece, these are known as `cover' versions. We describe a system that attempts to identify such a relationship between music audio recordings. To overcome variability in tempo, we use beat tracking to describe each piece with one feature vector per beat. To deal with variation in instrumentation, we use 12-dimensional `chroma' feature vectors that collect spectral energy supporting each semitone of the octave. To compare two recordings, we simply cross-correlate the entire beat-by-chroma representation for two tracks and look for sharp peaks indicating good local alignment between the pieces. Evaluation on several databases indicate good performance, including best performance on an independent international evaluation, where the system achieved a mean reciprocal ranking of 0.49 for true cover versions among top-10 returns.

Although the process of analyzing an audio recording of a music performance is complex and difficult even for a human listener, there are limited forms of information that may be tractably extracted and yet still enable interesting applications. We discuss melody-roughly, the part a listener might whistle or hum-as one such reduced descriptor of music audio, and consider how to define it, and what use it might be. We go on to describe the results of full-scale evaluations of melody transcription systems conducted in 2004 and 2005, including an overview of the systems submitted, details of how the evaluations were conducted, and a discussion of the results. For our definition of melody, current systems can achieve around 70% correct transcription at the frame level, including distinguishing between the presence or absence of the melody. Melodies transcribed at this level are readily recognizable, and show promise for practical applications