A comprehensive study based on MFCC and spectrogram for audio classification

被引：2

作者：

Rawat, Priyanshu ^{[1
]}

Bajaj, Madhvan ^{[1
]}

Vats, Satvik ^{[1
]}

Sharma, Vikrant ^{[1
]}

机构：

[1] Graphic Era Hill Univ, Dept Comp Sci & Engn, Dehra Dun, Uttarakhand, India

来源：

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES | 2023年 / 44卷 / 06期

关键词：

Music genre classification; Convolution Neural Network (CNN); Signal processing; Image processing; Spectrogram; Artificial Neural Network (ANN); Audio analysis; MODEL; RECOGNITION; DEPLOYMENT;

D O I：

10.47974/JIOS-1431

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

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.

引用

页码：1057 / 1074

页数：18

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