Academic literature on the topic 'Deep CCA'

Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different modalities that the multimodal-DL methods model on. The paper specially designs a multimodal-DL framework, the multimodal convolutional neural networks (multimodal-CNNs) for the recognition of ship-radiated noise. Then the paper proposes a strategy based on canonical correlation analysis (CCA-based strategy) to build a joint representation and recognition on the two different single-modality (acoustics modality and visual modality). The multimodal-CNNs and the CCA-based strategy are tested on real ship-radiated noise data recorded. Experimental results show that, using the CCA-based strategy, strong-discriminative information can be built from weak-discriminative information provided from a single-modality. Experimental results also show that as long as any one of the single-modalities can provide information for the recognition, the multimodal-DL methods can have a much better multiclass recognition performance than the DL methods. The paper also discusses the advantages and superiorities of the multimodal-Dl methods over the traditional methods for ship-radiated noise recognition.

Proper identification of different grape varieties by smart machinery is of great importance to modern agriculture production. In this paper, a fast and accurate identification method based on Canonical Correlation Analysis (CCA), which can fuse different deep features extracted from Convolutional Neural Network (CNN), plus Support Vector Machine (SVM) is proposed. In this research, based on an open dataset, three types of state-of-the-art CNNs, seven species of deep features, and a multi-class SVM classifier were studied. First, the images were resized to meet the input requirements of a CNN. Then, the deep features of the input images were extracted by a specific deep features layer of the CNN. Next, two kinds of deep features from different networks were fused by CCA to increase the effective classification feature information. Finally, a multi-class SVM classifier was trained with the fused features. When applied to an open dataset, the model outcome shows that the fused deep features with any combination can obtain better identification performance than by using a single type of deep feature. The fusion of fc6 (in AlexNet network) and Fc1000 (in ResNet50 network) deep features obtained the best identification performance. The average F1 Score of 96.9% was 8.7% higher compared to the best performance of a single deep feature, i.e., Fc1000 of ResNet101, which was 88.2%. Furthermore, the F1 Score of the proposed method is 2.7% higher than the best performance obtained by using a CNN directly. The experimental results show that the method proposed in this paper can achieve fast and accurate identification of grape varieties. Based on the proposed algorithm, the smart machinery in agriculture can take more targeted measures based on the different characteristics of different grape varieties for further improvement of the yield and quality of grape production.