A Novel Audio Representation for Music Genre Identification in MIR
Navin Kamuni Mayank Jindal Arpita Soni Sukender Reddy Mallreddy Sharath Chandra Macha
For Music Information Retrieval downstream tasks, the most common audio
representation is time-frequency-based, such as Mel spectrograms. In order to
identify musical genres, this study explores the possibilities of a new form of
audio representation one of the most usual MIR downstream tasks. Therefore, to
discretely encoding music using deep vector quantization; a novel audio
representation was created for the innovative generative music model i.e.
Jukebox. The effectiveness of Jukebox's audio representation is compared to Mel
spectrograms using a dataset that is almost equivalent to State-of-the-Art
(SOTA) and an almost same transformer design. The results of this study imply
that, at least when the transformers are pretrained using a very modest dataset
of 20k tracks, Jukebox's audio representation is not superior to Mel
spectrograms. This could be explained by the fact that Jukebox's audio
representation does not sufficiently take into account the peculiarities of
human hearing perception. On the other hand, Mel spectrograms are specifically
created with the human auditory sense in mind.