Grunnleggende konsepter
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.
Sammendrag
The study investigates the use of deep VQ-based audio representations, as introduced in the Jukebox model, for music genre classification tasks. It compares the performance of three transformer-based models - SpectroFormer (using Mel spectrograms), TokenFormer (using VQ tokens), and CodebookFormer (using VQ codebooks) - on the Free Music Archive (FMA) dataset.
The key findings are:
- Mel spectrograms outperform deep VQ-based representations in music genre classification, with the SpectroFormer model achieving significantly higher F1 scores compared to the token- and codebook-based models.
- The deep VQ-based models (TokenFormer and CodebookFormer) only slightly outperform the baseline performance, suggesting that the deep VQ representation may not be well-suited for capturing the subtleties relevant to human perception of music genres.
- The study hypothesizes that the non-linear and data-intensive nature of deep VQ representations makes them more challenging to learn effectively, especially with the relatively small dataset used in this study (compared to the large dataset used to train the original Jukebox model).
- The results highlight the advantages of Fourier-based audio representations, particularly Mel spectrograms, for music genre classification tasks, despite the potential benefits of deep VQ representations for music generation.
Statistikk
"The FMA dataset offers a comprehensive library of 106,574 recordings by 16,341 artists over 161 genres, curated by WFMU, America's longest-standing freeform radio station."
"The medium-sized dataset of 25,000 tracks is chosen for its suitability in providing a significant yet feasible amount of data."
Sitater
"Jukebox's successful use of it to generate music points to a potential NN application."
"Deep VQ's technological prowess—particularly its remarkable compression capabilities—is the driving force behind its exploration."