Multimodal image and audio music transcription

Optical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that aim at obtaining a structured digital representation from sheet music images and acoustic recordings, respectively. While these fields have traditionally evolved independently, the fact that both tasks may share the same output representation poses the question of whether they could be combined in a synergistic manner to exploit the individual transcription advantages depicted by each modality. To evaluate this hypothesis, this paper presents a multimodal framework that combines the predictions from two neural end-to-end OMR and AMT systems by considering a local alignment approach. We assess several experimental scenarios with monophonic music pieces to evaluate our approach under different conditions of the individual transcription systems. In general, the multimodal framework clearly outperforms the single recognition modalities, attaining a relative improvement close to $$40\%$$ 40 % in the best case. Our initial premise is, therefore, validated, thus opening avenues for further research in multimodal OMR-AMT transcription.

Towards a high robust neural network via feature matching

Image classification systems have been found vulnerable to adversarial attack, which is imperceptible to human but can easily fool deep neural networks. Recent researches indicate that regularizing the network by introducing randomness could greatly improve the model’s robustness against adversarial attack, but the randomness module would normally involve complex calculations and numerous additional parameters and seriously affect the model performance on clean data. In this paper, we propose a feature matching module to regularize the network. Specifically, our model learns a feature vector for each category and imposes additional restrictions on image features. Then, the similarity between image features and category features is used as the basis for classification. Our method does not introduce any additional network parameters than undefended model and can be easily integrated into any neural network. Experiments on the CIFAR10 and SVHN datasets highlight that our proposed module can effectively improve both clean data and perturbed data accuracy in comparison with the state-of-the-art defense methods and outperform the L2P method by 6.3$$\%$$ % , 24$$\%$$ % on clean and perturbed data, respectively, using ResNet-V2(18) architecture.