Re: Gsoc 2015: Intruduction

Hi Alastair,

Thanks for your attention.


*Description of “Emotion in music” task *


The task is the continuous emotion characterization task. The emotional
dimensions, arousal and valence (VA), should be determined for a given song
continuously in time. The quantization scale will be per frame (e.g., 1s).
It will provide a set of music licensed under Creative Commons from Free
Music Archive with human annotations. Participants upload the VA
predictions of testing set. The goal is to make the Pearson correlation as
high as possible and root mean square error as low as possible.


*Description of Dataset*


It uses an extension of 744 songs dataset developed for the same task at
Mediaeval 2013. The annotations are collected on Amazon Mechanical Turk.
Single workers provided A-V labels for clips from our dataset, consisting
of 744 30-second clips, which are extended to 45 seconds in the annotation
task to give workers additional practice. The labels will be collected at
1Hz. Workers are given detailed instructions describing the A-V space.


*Our working note*

http://ceur-ws.org/Vol-1263/mediaeval2014_submission_16.pdf


Our approach is also to use “low-level features” + ”SVR”. For modeling the
continuous emotions better, we adopted the “CCRF” model. Our results shows
a high Pearson correlation and low root mean square error. In fact we also
test other regression such as NN, KNN, but the performances of them are not
better than SVR+CCRF. But CCRF can’t be adopted for static emotion directly.


*About the emotion topic*


For the first part, I honestly don’t have an idea how to collect a large
training set with reasonable distribution for now. And I am curious about
how the existing 650,000 tracks's labels come from. However, I think
regarding emotion as a two-dimension space is a good way to build only one
model instead of building one model for each mood. "Arousal" is the
level/amount of physical response and "valence" is the emotional
"direction" of that emotion. The image below shows details.


http://doi.ieeecomputersociety.org/cms/Computer.org/dl/trans/ta/2012/02/figures/tta20120202371.gif


For the second part, I can see the low-level features you present are
complete, but they also make the dimension of features too high, which may
lead to over fitting and ask for larger dataset. I don’t know whether you
have adopted some dimensionality reduction methods such as PCA, LDA, NMF.
These may help. And for features, DNN may be a good way for exploring a
method of better performance. For classifier, maybe some types of neural
networks have a good performance. As far as I am concerned, long short-Term
memory based recurrent neural network (LSTM RNN) have advantages in music
emotion classification or regression.



Best regards,

Kang

2015-03-23 17:35 GMT+08:00 Alastair Porter <[email protected]>:

> Hi Kang,
> Thanks for your email.
> Do you have more results about your emotion in music task? What was your
> goal, and what did your results show?
>
> We talked a little bit about emotion in our initial blog post:
>
> http://blog.musicbrainz.org/2014/11/21/what-do-650000-files-look-like-anyway/
> And discovered that our existing results are not that great. We definitely
> want to address this topic more.
>
> For us, there are two parts to any of these training problems. The first
> part is to find a dataset that is representative of our topic. As you have
> pointed out, there may be a problem with using small datasets on a
> collection as large as AcousticBrainz.
> Do you have any ideas how we could collect a large training set?
>
> The second part to address is the actual training method. We're currently
> using SVM, with automatic feature selection based on the features present
> in our low-level data. Maybe you also have some ideas here about which
> training method is most effective. What did your results in your project
> show?
>
> Regards,
> Alastair
>
>
>
> On Sat, Mar 21, 2015 at 4:41 AM, 蔡康 <[email protected]> wrote:
>
>> Hi,
>>
>> My name is Kang Cai, graduate student of Peking University, major in
>> audio information processing.  Half a year ago, I took part in “Emotion
>> in Music “ task in “MediaEval 2014” and achieved good results.
>>
>>
>>
>> Recently, I want to participate in GSoC 2015. After searching for a long
>> time, I finally find the interesting project “AcousticBrainz”.  The
>> project’s main idea is to realize automatic tagging for music through
>> semi-supervised machine learning. For me, this project has three major
>> challenges. The first one is how to work well with existing algorithms to
>> realize it. The second one is the “big data”, which is different from the
>> small dataset I used for experiment in my lab. The last one is this is my
>> first time to apply for online cooperative project, kind of excited.
>> Although I’m not familiar with the existing framework of this project, I
>> wish I could have the chance to work on it.
>>
>>
>>
>> Best regards,
>>
>>
>>
>> Kang Cai
>>
>> _______________________________________________
>> MusicBrainz-devel mailing list
>> [email protected]
>> http://lists.musicbrainz.org/mailman/listinfo/musicbrainz-devel
>>
>
>
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