Musical Tempo Estimation Using a Multi-scale Network
This work addresses tempo estimation for music analysis, presenting an incremental improvement over prior single-step systems.
The paper tackles musical tempo estimation by proposing a Multi-scale Grouped Attention Network, which outperforms existing state-of-the-art methods on Accuracy1 in public datasets.
Recently, some single-step systems without onset detection have shown their effectiveness in automatic musical tempo estimation. Following the success of these systems, in this paper we propose a Multi-scale Grouped Attention Network to further explore the potential of such methods. A multi-scale structure is introduced as the overall network architecture where information from different scales is aggregated to strengthen contextual feature learning. Furthermore, we propose a Grouped Attention Module as the key component of the network. The proposed module separates the input feature into several groups along the frequency axis, which makes it capable of capturing long-range dependencies from different frequency positions on the spectrogram. In comparison experiments, the results on public datasets show that the proposed model outperforms existing state-of-the-art methods on Accuracy1.