MFCC based hybrid fingerprinting method for audio classification through LSTM

	MFCC based hybrid fingerprinting method for audio classification through LSTM
International Journal of Nonlinear Analysis and Applications
دوره 12، Special Issue، اسفند 2021، صفحه 2125-2136 اصل مقاله (988.1 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6049
نویسندگان
K. Banuroopa؛ D. Shanmuga Priyaa^*
Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India
چکیده
In this paper, a novel audio finger methodology for audio classification is proposed. The fingerprint of the audio signal is a unique digest to identify the signal. The proposed model uses the audio fingerprinting methodology to create a unique fingerprint of the audio files. The fingerprints are created by extracting an MFCC spectrum and then taking a mean of the spectra and converting the spectrum into a binary image. These images are then fed to the LSTM network to classify the environmental sounds stored in UrbanSound8K dataset and it produces an accuracy of 98.8\% of accuracy across all 10 folds of the dataset.
کلیدواژه‌ها
Audio fingerprinting؛ MFCC؛ Audio Classification؛ LSTM
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