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Processing MIDI formats). Three hundred algorithms have been evaluated since the beginning of MIREX. 1% average precision. Interestingly, the 2007 submissions moved from spectral-based “timbral similarity” toward more musical features, such as tonality, rhythm, and harmonic progressions, overcoming what were perceived by researchers as a performance ceiling. Another ﬁnding was that particular systems appear to have unique abilities that address speciﬁc subsets of queries, suggesting that hybrid approaches that combine the best aspects of the individual systems could improve performance.
While the RT evaluations focus on speech and text recognition, other CLEAR evaluation tasks include person, face, and vehicle tracking, head pose estimation, as well as acoustic scene analysis (Stiefelhagen 2007). Included are tasks performed in the visual, acoustic, and audio-visual domains for meeting room and surveillance data, thus effectively bridging the vision and speech research communities. While consistent annotation proved too difﬁcult, original plans were to include an unmanned aerial vehicle (UAV) person detection and tracking task using data from the DARPA Video Veriﬁcation of Identity (VIVID) program.
Cut and gradual transition). , fades) demonstrate lower precision (around 70– 90%) and recall (around 60–80%). Feature detection is a broad category of queries with a broad range of difﬁculty and includes people, such as Madeleine Albright or Bill Clinton, objects, such as trains, places, such as a beach (with water and shore visible) or road (paved or not), and events, such as an airplane taking off, violence between people and/or objects, or people walking or running. Smeaton et al. (2009) provides a summary of features and performance from TRECVid campaigns from 2002 to 2006.