Hi Sean, On Mon, 6 Jun 2011, Sean Liu wrote:
> Date: Mon, 06 Jun 2011 19:50:32 +0800 > > Hi, > > I am not sure if you are actively working on the project any more. I > hope you are. Yes, I am. I just lag horribly behind in updating the status and pages as I am terribly busy with my PhD among other things. I do maintain, use, and publish using MARF, however. At lease sporradically. Most recently in MARFCAT. > When I used your sample wave files, every thing works as exactly as you > wrote in the manual document. > > But when I changed to use my own set of wave files, the accuracy > dramatically dropped, I do have a lot more data than yours though. How much more data and what's the drop? > Have you ever tried to use different set of data, especially more data? Yes. I tried all kinds of stuff -- audio, text, images, binary files, and all kinds of experiments. However, you'd need to open up a bit more on the details of your setup. There are many factors that can contribute to good or poor recognition accuracy. - sample frequency? (8 kHz, 16 kHz?) - PCM data (2 bytes, more, less, which endian?) - sample quality is it realitively uniform? - do you do noise and silence removal (-noise, -silence options) - how many samples are used to train per class and test per class? (class ~ e.g. speaker, gender, etc.) - are you using mean clusters (the default), median clustering, or plain feature vectors? - ... A lot depends on correct data loading, interpretation, preprocessing, before feature extraction and classification. Also, if you mislabel your training data, you are in for trouble too (I am not sure how much data you actually have and if your labeling of them is correct). Also, please try the most recent version of MARF from the CVS itself; there's been bugs (or if you are using a .jar then pick the one from the recently released app). > And how was the performance if you did? Variable. For images it was relatively poor. For audio (voice), text and binaries it was much better. Here are some works describing experiments done at various times and diversity. You can just skip over to the result tables and then come back for a brief read about the experiments themselves. http://dx.doi.org/10.1145/1370256.1370262 http://arxiv.org/abs/1010.2511 http://www.intechopen.com/download/pdf/pdfs_id/12131 http://subs.emis.de/LNI/Proceedings/Proceedings140/gi-proc-140-007.pdf http://arxiv.org/abs/1006.3787 http://arxiv.org/abs/0912.5502 > Thanks, > > Sean Liu -- Serguei A. Mokhov, PhD Candidate | /~\ The ASCII Computer Science and Software Engineering & | \ / Ribbon Campaign Concordia Institute for Information Systems Engineering | X Against HTML Concordia University, Montreal, Quebec, Canada | / \ Email! ------------------------------------------------------------------------------ EditLive Enterprise is the world's most technically advanced content authoring tool. Experience the power of Track Changes, Inline Image Editing and ensure content is compliant with Accessibility Checking. http://p.sf.net/sfu/ephox-dev2dev _______________________________________________ marf-devel mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/marf-devel
