Normalize the recordings to have the same number of channels about free-spoken-digit-dataset

[Jakobovski]

I don't see any reason to have stereo samples. I can't imagine stereo being useful for anyone using this dataset. Therefore I think it best for all audio samples to be made mono.

Currently, there are no plans to normalize the samples. If you would like to do it, and update the README I would be happy to merge.

BTW what are you using this dataset for?

[cesarsouza]

Hi @Jakobovski, thanks for the answer! I am not sure I will have the time to normalize the samples either, but I could try to update the README with instructions for the next contributors.

Regarding the dataset use, I am currently using the dataset to create an example about how to do audio classification using Bag-of-Audio-Words and MFCC features for the Accord.NET Framework. The interface I had mentioned on issue #9 will be included as part of the project to enable users to download the dataset, and learn and test classification models on it, quickly.

By the way, do you have any baseline numbers of test set accuracy that I could compare against?

Regards,
Cesar

[Jakobovski]

Nice. It has been merged.

No, I dont have a baseline. Although it did some work with FSDD in this repo https://github.com/Jakobovski/decoupled-multimodal-learning

[cesarsouza]

Hi @Jakobovski,

Just to give a small update, I've just made a new release of the Accord.NET Framework adding the Free Spoken Digits Dataset to the list of datasets that can be downloaded and interacted with from C#. The documentation page for the Accord.DataSets.FreeSpokenDigitsDataset class can be found here: http://accord-framework.net/docs/html/T_Accord_DataSets_FreeSpokenDigitsDataset.htm.

I've also added an example showing how to use the FSDD to learn models for audio classification through BoAW and MFCC (bottom of the page). Just for the information of whoever could be interested, without optimizing hyper-parameters, I've been able to achieve 0.97 training error and 0.86 testing error (on the provided training and testing sets specified by the FSDD).

I am sure those numbers could be improved easily through better hyper-parameter search, by using better normalization options, or by using a better representation (such as the FV instead of BoW). Or, of course, by using deep learning models instead of plain SVMs.

Regards,
Cesar

[Jakobovski]

Very cool. If you want you can add Accord.NET to the FSDD README

[cesarsouza]

Thanks @Jakobovski, since you allowed, I've just did it with #13