Perform feature extraction on a descriptor file instead of an audio file

[NaturalFigurehead]

Is there a way to train a model, then perform feature extraction on a descriptor file of an audio file instead of the actual audio file itself?

My problem is that users will submit their audio files to me for analysis. I analyse them and send back the results. Then I delete the audio file because it's not mine and I can't store it. But if I can export some sort of descriptor file for that audio file, then later I'd like to be able to train a new model and extract the features for that descriptor.

Is something like this possible with this library?

[slychief]

Hi,

I'm not quite shure if I understand your question. When you say you are analysing tracks, do you mean, that you perform a classification or detection task such as genre classification or mood prediction?

Which modules are you using from this repository?

[audiofeature]

Hi Oliver, Our library allows to perform those 2 steps: 1) feature extraction (using rp_extract.py or rp_extract.batch.py) 2) classification (using rp_classify.py) Yes you can store the results of step 1 to do or redo classification (step 2) later. rp_extract.batch.py will store those features in a CSV or HDF5 file you could also build a wrapper around rp_extract.py to store the feature in a database.

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Am 08.08.2018 um 14:16 schrieb Oliver Reznik ***@***.***>: Is there a way to train a model, then perform feature extraction on a descriptor file of an audio file instead of the actual audio file itself? My problem is that users will submit their audio files to me for analysis. I analyse them and send back the results. Then I delete the audio file because it's not mine and I can't store it. But if I can export some sort of descriptor file for that audio file, then later I'd like to be able to train a new model and extract the features for that descriptor. Is something like this possible with this library? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#26>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ALHE6RtLKjJ--ggBLhV_AsQ394hrCnslks5uOtaEgaJpZM4Vz0gE>.

-- Thomas Lidy TU Wien - Vienna University of Technology Institute of Software Technology and Interactive Systems Favoritenstraße 9-11/188 A-1040 Vienna, Austria http://www.ifs.tuwien.ac.at/~lidy <http://www.ifs.tuwien.ac.at/~lidy>

[NaturalFigurehead]

@audiofeature cool that's what I was looking for. Looks like this uses the classify function? Where do I get the model object to pass into it? And then for feature I just pass a path to one of the three feature files?

I'd read through this tutorial (http://nbviewer.ipython.org/github/tuwien-musicir/rp_extract/blob/master/RP_extract_Tutorial.ipynb) but it seems to not be working.

[audiofeature]

I have pushed a .v4 file of the tutorial, in case a read error with newer Jupyter version was the problem. You can do those steps 1) Feature extraction: ./rp_extract_batch.sh <folder_with_audio_files> <output_feature_filename_no_extension> It will extract the default features RP, SSD, RH and create 3 output files with those extensions. (use other parameters to get other feature types) alternatively from rp_extract import rp_extract into your code 2) Classification a) train your own model: (refer to README.md -> "Train a model“): ./rp_classify.py <folder_with_audio_files> <output_model_file> --classfile <class_file> will analyze the audio files like in step 1 then create a classifier model with SVM classifier classfile: you have to provide a tab-separted file where each input filename is listed with relative path and a tab + a label of the genre category (string) b) make predictions / classifications: ./rp_classify.py <input_path> <model_file> <output_file> <input_path> individual audio file or folder <model_file> your own model file if you created one in step 2.a; if omitted, a pretrained model from folder models/GTZAN.* will be used (this one was generated however with sklearn 0.17 so you’d have to downgrade to that version to use it); <output_file> file to write the predictions to; if omitted, will print on screen best Thomas

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Am 10.08.2018 um 01:12 schrieb Oliver Reznik ***@***.***>: @audiofeature <https://github.com/audiofeature> cool that's what I was looking for. Looks like this uses the classify function? Where do I get the model object to pass into it? And then for feature I just pass a path to one of the three feature files? I'd read through this tutorial (http://nbviewer.ipython.org/github/tuwien-musicir/rp_extract/blob/master/RP_extract_Tutorial.ipynb <http://nbviewer.ipython.org/github/tuwien-musicir/rp_extract/blob/master/RP_extract_Tutorial.ipynb>) but it seems to not be working. — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#26 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ALHE6RsFjb5dmV2voRpOOLaMmuQlQ0Tdks5uPMHIgaJpZM4Vz0gE>.

-- Thomas Lidy TU Wien - Vienna University of Technology Institute of Software Technology and Interactive Systems Favoritenstraße 9-11/188 A-1040 Vienna, Austria http://www.ifs.tuwien.ac.at/~lidy <http://www.ifs.tuwien.ac.at/~lidy>