mfcc based hybrid fingerprinting method for audio classification through lstm

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.