Skip to content Skip to navigation

OpenStax_CNX

You are here: Home » Content » Music Classification by Genre: Overall Results

Navigation

Recently Viewed

This feature requires Javascript to be enabled.
 

Music Classification by Genre: Overall Results

Module by: Melodie Chu, Christopher Hunter, Mitali Banerjee. E-mail the authors

Summary: When tested with the training vectors, the system is 87.5% accurate. Higher accuracy implies that the system has memorized the training set and is unable to generalize when given new inputs.

Figure 1: Overall, bandwidth is a good detector for jazz and rap, but poorer in distinguishing between classical, punk, techno, and country, which all have about the same bandwidth.
Bandwidth
Bandwidth (standarddeviation.gif)
Figure 2: Rap is the only genre that can be distinguished by its beat. Classical and jazz have much higher variability in tempo, since they often consist of a long piece subdivided into sections.
Beat (Tempo)
Beat (Tempo) (sdbeat.gif)
Figure 3: Though difficult to isolate any one genre, this analysis tool does separate them into two main categories: (1) Classical, Punk and Country (2) Techno, Jazz, and Rap.
Frequency Cutoff
Frequency Cutoff (sdfreqcutoff.gif)
Figure 4: While freqsmooth does give a different value for each genre, it also gives a radically different value for songs within a given genre. In other words, it does not give a good representation of a genre as a whole. Given the plus and minus standard deviation bars, each genre overlaps heavily.
Frequency Smoothness
Frequency Smoothness (sdfreqsmoothness.gif)
Figure 5: Like one would expect, classical had the smallest error of any genre tested. This should be intuitive since it uses the lower frequency part of the spectrum. One can think of classical music as being very fluid with few sudden changes in frequency. Conversely, punk and jazz had the highest amount of error, which is a good indication of higher frequencies being utilized. Compared to classical music, these genres are much less fluid and often exhibit rapid changes in tempo. Somewhere between these two extremes are techno, rap and country.
High Pass Filtering
High Pass Filtering (highpass.gif)
Figure 6: The standard deviations of rap and techno are very distinct, whereas the others are all about the same value. Although the average total power of techno may not be a good indicator, the standard deviation should be able to pick out techno.
Total Power
Total Power (sdtotalpower.gif)

Content actions

Download module as:

PDF | EPUB (?)

What is an EPUB file?

EPUB is an electronic book format that can be read on a variety of mobile devices.

Downloading to a reading device

For detailed instructions on how to download this content's EPUB to your specific device, click the "(?)" link.

| More downloads ...

Add module to:

My Favorites (?)

'My Favorites' is a special kind of lens which you can use to bookmark modules and collections. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need an account to use 'My Favorites'.

| A lens I own (?)

Definition of a lens

Lenses

A lens is a custom view of the content in the repository. You can think of it as a fancy kind of list that will let you see content through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

| External bookmarks