Authors :
Nallaperumal.K
Volume/Issue :
Volume 6 - 2021, Issue 10 - October
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3C9qzsz
Abstract :
Deep learning approaches allow us to look at
signal processing problems from a different angle, which
is currently widely disregarded in the music business.
Audio is intrinsically more time-sensitive than film. You
can never assume that a pixel in a spectrogram belongs to
a single object. Due to the fact that audio is always
transparent, spectrograms show all audible sounds
overlapping in the same frame. It has been demonstrated
that spectrograms can be processed as images and neural
style transfer can be performed with CNNs, although the
results have not been as exact as they have been for visual
images. We should focus our efforts on developing more
accurate representations.
Keywords :
Autoencoders, Autocorrelogram, Encoding, Audio Encoders, RNN Autoencoder, Audio Frequency, Auto Correlation And Convolution, Cross Fold Validation
Deep learning approaches allow us to look at
signal processing problems from a different angle, which
is currently widely disregarded in the music business.
Audio is intrinsically more time-sensitive than film. You
can never assume that a pixel in a spectrogram belongs to
a single object. Due to the fact that audio is always
transparent, spectrograms show all audible sounds
overlapping in the same frame. It has been demonstrated
that spectrograms can be processed as images and neural
style transfer can be performed with CNNs, although the
results have not been as exact as they have been for visual
images. We should focus our efforts on developing more
accurate representations.
Keywords :
Autoencoders, Autocorrelogram, Encoding, Audio Encoders, RNN Autoencoder, Audio Frequency, Auto Correlation And Convolution, Cross Fold Validation
