Jinliang Dai

Automatic piano transcription (APT) is a challenging problem in music information retrieval. In recent years, most APT approaches have been based on neural networks and have demonstrated higher performance. However, most previous works utilize a short-time Fourier transform (STFT) spectrogram as input, which results in a noisy spectrogram due to the mixing of harmonics from concurrent notes. To address this issue, a novel APT network based on two spectrograms is proposed. Firstly, the Mel cyclic and Mel STFT spectrograms of the piano musical signal are computed to represent the mixed audio. Next, separate modules for onset, offset, and frame-level note detection are constructed to achieve distinct objectives. To capture the temporal dynamics of notes, an axial attention mechanism is incorporated into the frame-level note detection modules. Finally, a multi-feature fusion module is introduced to aggregate different features and generate the piano note sequences. In this work, the two spectrograms provide complementary information, the axial attention mechanism enhances the temporal relevance of notes, and the multi-feature fusion module incorporates frame-level note, note onset, and note offset features together to deduce final piano notes. Experimental results demonstrate that the proposed approach achieves higher accuracies with lower error rates in automatic piano transcription compared with other reference approaches.

In this paper, a graph convolutional network is constructed and applied for bearing fault diagnosis. Specifically, the constant-Q transform (CQT) is first adopted for spectral analysis of vibration signals, where the frequencies are distributed in the logarithmic scale. Varied frequency resolutions can be obtained to satisfy the spectral resolution requirement and reduce signal dimension. Afterwards, the CQT spectrum is modeled by a graph, where nodes are frequency bins and edges reflect the inner relationship of different bins. There are edges between the fundamental and harmonic components. Then, a two-layer graph convolutional network (GCN) is utilized to assess the significance of vibration sources within the mixed signals. Finally, the bearing faults are determined according to the output of the GCN. To the best of our knowledge, this is the first work to model the vibration signal in this graph structure. The advantage of this approach lies in the simplification of edge definitions, facilitating shared connectivity relationships between the fundamental frequency and harmonics. Its performance was compared with another state-of-the-art fault diagnosis model. Experimental results demonstrate that the proposed model obtains higher accuracy, and it is more effective in extracting discriminative features.