State-of-the-art strided audio encoders impose predictable alias-collapse and resolution bottlenecks on frequency primitives; Gabor Latent Refactorization recovers much of the lost separability post-hoc.
Automatic tagging using deep convolutional neural networks
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abstract
We present a content-based automatic music tagging algorithm using fully convolutional neural networks (FCNs). We evaluate different architectures consisting of 2D convolutional layers and subsampling layers only. In the experiments, we measure the AUC-ROC scores of the architectures with different complexities and input types using the MagnaTagATune dataset, where a 4-layer architecture shows state-of-the-art performance with mel-spectrogram input. Furthermore, we evaluated the performances of the architectures with varying the number of layers on a larger dataset (Million Song Dataset), and found that deeper models outperformed the 4-layer architecture. The experiments show that mel-spectrogram is an effective time-frequency representation for automatic tagging and that more complex models benefit from more training data.
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Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
citing papers explorer
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Structural Bottlenecks on Frequency Representation in End-to-End Audio Models
State-of-the-art strided audio encoders impose predictable alias-collapse and resolution bottlenecks on frequency primitives; Gabor Latent Refactorization recovers much of the lost separability post-hoc.
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Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.