A Classifier for Guitar Tabs

8/10/2019 A Classifier for Guitar Tabs

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A Classifier For Guitar Tabs

INTRODUCTION/MOTIVATION:

I built a prediction system for the difficulty of a guitar tabs or sheet music. A

guitar tab is a collection of notes written specifically for the guitar in the tablature

format. The following image is an example of what a tab looks like:

THE DATA SET:

The first big problem of this project is curating the data set. Ultimate-guitar.com is

the go-to site for any guitarist looking for sheet music. The problem with

Ultimate-guitars difficulty measurement system is that it doesnt use a classifier,

instead when a user submits a sheet they select the difficulty of the piece

themselves. This adds large amounts of bias to the sites difficulty system becausedifficulty is a relative term and the site is relying exclusively on a single users

determination of whether a song is tough or not. To combat this, the method I

went with is hand curating a dataset from scratch. I downloaded and hand-labeled

67 randomly selected sheets of music from Ultimate-Guitar.com. Each rating is on

a scale of 1 5, 1 meaning the song is very easy and 5 meaning the song is

extremely difficult. To improve the robustness of the database I asked another


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friend to hand-label each of the data points I labeled already. The result was a 67

row and 2 column data matrix. This was definitely a challenging data set to get

performance on. Ive had some success with the data, but nothing super

spectacular. Im convinced though there probably is more structure to the data that

Im just not seeing.

My classifier pipeline has several stages.

First, I build a feature table from the data. This is an N x M matrix where N is the

number of tabs (67) and M is the number of features. Ive been playing around

with a set of M=15 features which Ill describe later.

Second, I run feature selection on the features to select the features that are most

useful. Obviously, you dont want to use all 15 features because some are much

more noisy than others. Feature selection is a process to select the features that

appear most useful in classification.

Third, I run different classifier algorithms, hoping to find the right one to model

the data. I have played with decision trees, multinomial logistic regression and

KNN on my feature set.

1. Feature Table Creation

I have been playing with the following 15 features, some obviously more useful

than others. I have sorted the features into categories.

Track Length Features

-Mean length of track in tab

-Max length of a track in tab

-Min length of a track in tab

-Number of tracks in tab

Note/chord Features

-Mean number of unique notes/chords in track

-Max number of unique notes/chords in track

Chord Features a chord is defined as playing more than one string

-Median Number of chords in tracks

-Maximum Number of chords in tracks

-Median number of unique chords in tracks.

-Max number of unique chords in tracks

-Median frequency of chords playing (median # of times a chord was played, out

of playing of all chords)


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-Maximum frequency of chords playing

-Minimum frequency of chords playing

Auxiliary Features

-Band name (as a unique id) intuition that maybe certain bands produce harder

music than others and reputation factors into our labeling.

From this giant feature table, I find the most useful features using the following

feature selection strategy.

2. Feature Selection

For this section I used a lot of help from Dr. Ravikumar and a paper I found on

piano sheet music classification (I will cite this paper later in the article). A lot of

the math goes over my head but I feel like I understand how to use these methods

as a tool to achieve my goal.

First, in building a classifier, often what people care about is having class-

conditional density distributions be as divergent (i.e. different) as possible.

What I have plotted is the histogram of training data that falls under that feature

for each of our 5 labels. Notice that the distributions are quite distinct and dont

overlap much. This means that this feature separates the labels well.

This feature turned out to be maximum number of unique chords in piece. This is

the best histogram I could produce and this one isnt very good. An ideal

distribution would be 5 curve that are distinct and dont overlap.


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Conversely, here is an example of a not-so-great feature:

Notice the distributions overlap completely for all labels. Thus, this feature isnt

useful in telling the different labels apart.

At first I just eyeballed the plots to see which distribution was the most distinct but

then I found KL-Divergence. KL(P||Q) is a way to say non-parametrically how

different two distributions P and Q are. Its a distance metric between distributions

that is based on information entropy. It is quite useful in state of the art Machine

Learning.

One difficulty with the vanilla KL-Divergence formula is that it is not symmetric.Thus, if you swap P and Q then you will get a different answer.

KL(P||Q)!=KL(Q||P). To solve for this, people often use the Symmetrized KL-

Divergence (SKL) = KL(P||Q) + KL(Q||P). Basically, this metric is just adding

both ways.


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These are the features with the maximum SKL for my friend and me.

Friends top features Ankits top features

Median Number of Unique Chords Band Name

Maximum Length of Track Median Number of Unique Chords

Maximum Skewness of Rest Distribution Mean Weighted Average Duration

Maximum number of unique chords Number of Tracks

Band Name Max weighted average duration

It is interesting the differences in features that give rise to the cleanest separation

of difficulty rankings for our individual labeling. My friend seems to really like

looking for unique chords, and find fast or long pieces difficult. I think my friend

also consider pieces with lots of sixteenth and eight notes hard. I think I have a

bias towards band name.


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Classification

These are the classification accuracies Im getting, on average, via 10-fold cross-

validation in using the entire set of 15 features:

Classifier Accuracy on

friends Data

Accuracy on

Ankits Data

Decision Trees [matlab: classregtree] 37.86% 27.86%

Multinomial Logistic Regression

[matlab: mlrfit]

15.48% 9.52%

K Nearest Neighbor [matlab:

knnclassify]

20.21% 25.00%


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These are the classification accuracies Im getting, on average, via cross-validation

on the data using ONLY the top 5 feature selections:

Classifier Accuracy on

friends Data

Accuracy on

Ankits Data

Decision Trees [matlab: classregtree] 44.29% 31.19%

Multinomial Logistic Regression[matlab: mlrfit]

42.42% 29.29%


knnclassify]

26.43% 34.52%

Multi-class SVM [libsvm] ~36% ~18%


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Here are accuracies if I clump labels 2,3,4 into 1 label. Thus, we reduce the 5

labels to 3 and also use feature selection.

Classifier Accuracy on friends

Data Accuracy on Ankit

Data

Decision Trees [matlab: classregtree] 14.29%

6.80%

Multinomial Logistic Regression[matlab: mlrfit]

76.90%62.38%


knnclassify]

66.90% 59.67%

Overall I found this to be a very tough data set. The only way Ive been able to getabove chance performance is with lumping the middle labels into 1 giant label.

This parallels the intuition that there is relatively clean separation between labels 1

and 5 with several features but it is hard to tell the middle labels apart.

CITATIONS:

Chiu, Shih-Chuan, and Min-Syan Chen. "A Study on Difficulty Level Recognition of

Piano Sheet Music."IEEE(2012): n. pag.IEEE Xplore. Web.

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A Classifier for Guitar Tabs