9,145 research outputs found
The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use
The GTZAN dataset appears in at least 100 published works, and is the
most-used public dataset for evaluation in machine listening research for music
genre recognition (MGR). Our recent work, however, shows GTZAN has several
faults (repetitions, mislabelings, and distortions), which challenge the
interpretability of any result derived using it. In this article, we disprove
the claims that all MGR systems are affected in the same ways by these faults,
and that the performances of MGR systems in GTZAN are still meaningfully
comparable since they all face the same faults. We identify and analyze the
contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has
been used in MGR research, and find few indications that its faults have been
known and considered. Finally, we rigorously study the effects of its faults on
evaluating five different MGR systems. The lesson is not to banish GTZAN, but
to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference
Audiovisual annotation procedure for multi-view field recordings
Audio and video parts of an audiovisual document interact to produce an audiovisual, or multi-modal, perception. Yet, automatic analysis on these documents are usually based on separate audio and video annotations. Regarding the audiovisual content, these annotations could be incomplete, or not relevant. Besides, the expanding possibilities of creating audiovisual documents lead to consider different kinds of contents, including videos filmed in uncontrolled conditions (i.e. fields recordings), or scenes filmed from different points of view (multi-view). In this paper we propose an original procedure to produce manual annotations in different contexts, including multi-modal and multi-view documents. This procedure, based on using both audio and video annotations, ensures consistency considering audio or video only, and provides additionally audiovisual information at a richer level. Finally, different applications are made possible when considering such annotated data. In particular, we present an example application in a network of recordings in which our annotations allow multi-source retrieval using mono or multi-modal queries
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