Open Access
Research Article
A comprehensive study based on MFCC and spectrogram for audio classification
Priyanshu Rawatpriyanshurawat.cse.20011683@gehu.ac.inDepartment of Computer Science and Engineering Graphic Era Hill UniversityDehradun, Uttarakhand, IndiaView full profile → , Madhvan Bajajmadhvanbajaj.cst.ml.ai.20011249@gehu.ac.inDepartment of Computer Science and Engineering Graphic Era Hill UniversityDehradun, Uttarakhand, IndiaView full profile → , *Satvik VatsCorresponding authorsvcse@mmmut.ac.inDepartment of Computer Science and Engineering Graphic Era Hill UniversityDepartment of Computer Science & Engineering Madan Mohan Malaviya University of TechnologyGorakhpur, Uttar Pradesh, 273010, India0000-0002-9422-4915View full profile → , Vikrant Sharmavsharma@gehu.ac.inDepartment of Computer Science and Engineering Graphic Era Hill UniversityDehradun, Uttarakhand, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 04 Sep 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1431
- Pages:
- 1057–1074
Abstract
Music Assortment is a music information retrieval (MIR) function to decide music connotation computationally. In recent years, deep neural networks have been proven to be effective in numerous classification tasks, including music genre categorisation. In this paper, we employ a comparative study between the two different music classification techniques. The first technique uses the audio’s spectrogram image and computes the music’s genre based on its spectrogram, using the CNN model trained on the spectrograms. The second approach computes the MFCC’s (Mel-Frequency Cepstral Coefficients) musical features and utilises them to classify the music using ANN. This paper aims to study the two algorithms closely against different audio signals and check the performance report of the above-mentioned techniques to see which of them is better for music genre classification.
Keywords
Subject Classifications
68T07
References
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