We evaluate our tag recommendation system on CAL-500 and a large-scale data set (N = 77,448 songs) generated by crawling Youtube and Last.fm. For processing large-scale music data sets, we design parallel algorithms based on the MapReduce framework to perform large-scale music content and social tag analysis, train a model, and compute tag similarity. In the File menu, click 'Add Directory' and choose.
Download mp3tag, another free software, which is a dedicated music metadata editor. Our system is designed for large-scale deployment, on the order of millions of objects. m4a files if they are not already in that format. To the best of our knowledge, this is the first method to consider Explicit Multiple Attributes for tag recommendation. We propose a scheme for tag recommendation using Explicit Multiple Attributes based on tag semantic similarity and music content. Many of these are underrepresented by current tag recommenders. Once the user uploads or browses a song, the system recommends a list of relevant tags in each attribute independently. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. Explicit music are tracks or releases that are labelled with the explicit logo, and are simply a way for fans to know if your music contains any swear words. In our approach, the attribute space is explicitly constrained at the outset to a set that minimizes semantic loss and tag noise, while ensuring attribute diversity. Music attributes encompass any number of perceived dimensions, for instance vocalness, genre, and instrumentation. However, current methods do not consider diversity of music attributes, often using simple heuristics such as tag frequency for filtering out irrelevant tags.
Offhand I don't know of a free-to-use Mac tag tool with the same power as Mp3tag.
If you run Parallels you could install Mp3tag, tweak the content of the ITUNESADVISORY property (0 None, 2 Clean, 4 Explicit), remove the file from iTunes, then add it back again. Towards addressing these shortcomings, tag recommendation for more robust music discovery is an emerging topic of significance for researchers. iTunes doesn't provide any way to change such tags. Such collaborative intelligence, however, also generates a high degree of tags unhelpful to discovery, some of which obfuscate critical information. Social tagging can provide rich semantic information for large-scale retrieval in music discovery. MM'10 - Proceedings of the ACM Multimedia 2010 International Conference : 401-410. Large-scale music tag recommendation with explicit multiple attributes. Zhao, Z.,Wang, X.,Xiang, Q.,Sarroff, A.M.,Li, Z.,Wang, Y. Large-scale music tag recommendation with explicit multiple attributes