adityadutt / music-genre-classification

Classify music in two categories progressive rock and non-progressive rock using mfcc features, MLP, and CNN.

Music Genre Classification

Classify music into two categories progessive rock and non-prog rock.

Firstly, minimum duration song, min_duration was found. Then, all songs were divided into chunks of minimum of 30 seconds or min_duration. Librosa is used to analyze music extract frequencies. It is a Python module to analyze audio signals in general but geared more towards music. It includes the nuts and bolts to build a MIR (Music information retrieval) system. After building features, two approaches were considered to build genre classifier

• Extract a Mel spectrogram of song chunk and then design a convolution neural net to run on input spectrograms. Spectrograms of a prog and non prog song are shown in Fig. 1 and Fig 2. The accuracy with spectrograms was 70-73%.

• Extract 21 Mfcc features, zero crossing rate, chroma frequencies, spectral bandwidth, spectral centroid, roll off for each chunk. Then, all the features were appended into csv file using pandas. Create a model that uses LSTM with 2 layers and runs on input features. The accuracy with LSTM was 80-85%. An image of features is shown in Fig. 2.

• Fig1. Spectrogram of non-prog rock music chunk

• Fig1. Spectrogram of prog rock music chunk

Steps to run the project -

1. To train the model run- python LSTM.py
2. TO validate model run- python validate_model.py
3. To test model run- python test_model.py

Files

All the training features are stored in training_features.py All the validation features are stored in validation_features.py All the test features are stored in test_features.py All the test djent features are stored in test_djent_features.py

Authors

• Aditya Dutt
• Richa Dutt
• DingKang Wang
• Bin XU
• Kun Shi