|
--- |
|
license: mit |
|
title: Multi Label Music Genre Classifier |
|
sdk: streamlit |
|
sdk_version: 1.30.0 |
|
app_file: app.py |
|
emoji: 🚀 |
|
colorFrom: gray |
|
colorTo: blue |
|
pinned: false |
|
--- |
|
# Multi Label Music Genre Classifier |
|
|
|
An extension to my project of: [Single Label Music Genre Classifier](https://github.com/Hetan07/Single-Label-Music-Classifier) |
|
|
|
I was primarily interested on how prediction (or classification) |
|
would work in case if a sample can be classified into multiple classes at the same time |
|
which is what most of the time is the case with music |
|
|
|
Majority of the work done went into actually making the proper dataset to work and train upon. |
|
What I wanted was a dataset similar to the one I worked upon during single label classification. |
|
GTZAN has no dataset for mulit-label purposes, and so I had to make one |
|
|
|
A dataset, [MuMU](https://www.upf.edu/web/mtg/mumu) has the mulit-label tags but works on different components or features. |
|
So I decided to create a GTZAN-like dataset but with multi-labels |
|
|
|
--- |
|
|
|
### Dataset Creation |
|
|
|
The work done for creating the dataset were: |
|
|
|
- Downloading the appropriate songs taken randomly from the MuMu dataset in sampled manner from ~80 genres (tags) |
|
- Data Cleaning which included to clean and replace the download songs as many of them were things such as album intros, interludes or skits |
|
- There were also issues where the song required was not available on any platform and so had to appropriately replaced for another proper track or I had to manually search and download |
|
- Each file had to properly checked to prevent any distortion or disturbances |
|
- Applying feature extraction on each downloaded song using the *librosa* library |
|
- Reducing the labels from ~80 to around ~15 |
|
|
|
There was also an issue: MuMu dataset has no Classical Genre and thus it had to be added manually. |
|
|
|
In the end I decided to have feature extraction work on 3 second samples and thus have around ~24000 samples. |
|
|
|
I have linked the actual dataset created from all the steps if anyone wishes to work upon it |
|
|
|
--- |
|
|
|
For this task I decided to primarily work with neural networks and experimented with various architecture |
|
|
|
The models I trained are: |
|
|
|
- ANN |
|
- ANN with Batch Normalization |
|
- CNN |
|
- CRNN |
|
|
|
|
|
The genres classifed are the following: |
|
|
|
- Metal |
|
- Jazz |
|
- Blues |
|
- R&B |
|
- Classical |
|
- Reggae |
|
- Rap & Hip-Hop |
|
- Punk |
|
- Rock |
|
- Country |
|
- Bebop |
|
- Pop |
|
- Soul |
|
- Dance & Electronic |
|
- Folk |
|
|
|
I have also deployed this on Hugging Face using streamlit. If one wishes, he can test and play around with different music tracks. |
|
|
|
--- |
|
|
|
All of this took me around 3-4 days but in retrospect, I realize that some parts have been slightly rushed. |
|
An in-depth analysis of data is further required along with more data. This task as a lot of potential for beginners and experts alike |
|
|
