  Recently, multi-band spectrogram-based approaches such as Band-Split RNN
(BSRNN) have demonstrated promising results for music source separation. In our
recent work, we introduce the BS-RoFormer model which inherits the idea of
band-split scheme in BSRNN at the front-end, and then uses the hierarchical
Transformer with Rotary Position Embedding (RoPE) to model the inner-band and
inter-band sequences for multi-band mask estimation. This model has achieved
state-of-the-art performance, but the band-split scheme is defined empirically,
without analytic supports from the literature. In this paper, we propose
Mel-RoFormer, which adopts the Mel-band scheme that maps the frequency bins
into overlapped subbands according to the mel scale. In contract, the
band-split mapping in BSRNN and BS-RoFormer is non-overlapping and designed
based on heuristics. Using the MUSDB18HQ dataset for experiments, we
demonstrate that Mel-RoFormer outperforms BS-RoFormer in the separation tasks
of vocals, drums, and other stems.
