  In this work, we aim to improve the expressive capacity of waveform-based
discriminative music networks by modeling both sequential (temporal) and
hierarchical information in an efficient end-to-end architecture. We present
MuSLCAT, or Multi-scale and Multi-level Convolutional Attention Transformer, a
novel architecture for learning robust representations of complex music tags
directly from raw waveform recordings. We also introduce a lightweight variant
of MuSLCAT called MuSLCAN, short for Multi-scale and Multi-level Convolutional
Attention Network. Both MuSLCAT and MuSLCAN model features from multiple scales
and levels by integrating a frontend-backend architecture. The frontend targets
different frequency ranges while modeling long-range dependencies and
multi-level interactions by using two convolutional attention networks with
attention-augmented convolution (AAC) blocks. The backend dynamically
recalibrates multi-scale and level features extracted from the frontend by
incorporating self-attention. The difference between MuSLCAT and MuSLCAN is
their backend components. MuSLCAT's backend is a modified version of BERT.
While MuSLCAN's is a simple AAC block. We validate the proposed MuSLCAT and
MuSLCAN architectures by comparing them to state-of-the-art networks on four
benchmark datasets for music tagging and genre recognition. Our experiments
show that MuSLCAT and MuSLCAN consistently yield competitive results when
compared to state-of-the-art waveform-based models yet require considerably
fewer parameters.
