
This study explores the potential of deep vector quantization (VQ)-based audio representations, as used in the Jukebox model, for music genre identification tasks, and compares their performance to the well-established Mel spectrogram approach.


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exploring-jukebox-a-novel-audio-representation-for-music-genre-identification-in-mir


Exploring Jukebox: A Novel Audio Representation for Music Genre Identification in MIR


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Large language models (LLMs) show promise for music genre classification (MGC) in a zero-shot setting, particularly WavLM, but the audio spectrogram transformer (AST) model currently outperforms all others, highlighting the strength of transformer architectures in music information retrieval.


a-comparative-study-of-large-language-models-and-traditional-architectures-for-zero-shot-music-genre-classification


A Comparative Study of Large Language Models and Traditional Architectures for Zero-Shot Music Genre Classification
