Music Genre Classification using Spectral Analysis Techniques With Hybrid Convolution-Recurrent Neural Network
Faiyaz Ahmad1, Sahil2
1Faiyaz Ahmad, Computer Engineering, Jamia Millia Islamia, New Delhi, India.
2Sahil, Computer Engineering, Jamia Millia Islamia, New Delhi, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 149-154 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3956119119/2019©BEIESP | DOI: 10.35940/ijitee.A3956.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this work, the objective is to classify the audio data into specific genres from GTZAN dataset which contain about 10 genres. First, it perform the audio splitting to make it signal into clips which contains homogeneous content. Short-term Fourier Transform (STFT), Mel-spectrogram and Mel-frequency cepstrum coefficient (MFCC) are the most common feature extraction technique and each feature extraction technique has been successful in their own various audio applications. Then, these feature extractions of the audio fed to the Convolution Neural Network (CNN) model and VGG16 Neural Network model, which consist of 16 convolution layers network. We perform different feature extraction with different CNN and VGG16 model with or without different Recurrent Neural Network (RNN) and evaluated performance measure. In this model, it has achieved overall accuracy 95.5% for this task.
Keywords: GTZAN, Short-term Fourier Transform (STFT), Mel-spectrogram, Mel-frequency cepstrum coefficient (MFCC), Convolution Neural Network, VGG16, Recurrent Neural Network (RNN)
Scope of the Article: Classification