a2c [1910.11117] Graph Representation learning for Audio & Music genre Classification

Enable JavaScript to see more content

arXiv2code // top new 14d 1m 2m 3m // Enable JavaScript to see more content

1

[share]	Go to arXiv Download as Jupyter Notebook	2019-10-25
[1910.11117] Graph Representation learning for Audio & Music genre Classification Abstract Music genre is arguably one of the most important and discriminative information for music and audio content. Visual representation based approaches have been explored on spectrograms for music genre classification. However, lack of quality data and augmentation techniques makes it difficult to employ deep learning techniques successfully. We discuss the application of graph neural networks on such task due to their strong inductive bias, and show that combination of CNN and GNN is able to achieve state-of-the-art results on GTZAN, and AudioSet (Imbalanced Music) datasets. We also discuss the role of Siamese Neural Networks as an analogous to GNN for learning edge similarity weights. Furthermore, we also perform visual analysis to understand the field-of-view of our model into the spectrogram based on genre labels. ‹ Figure 1: (a) Confusion Matrix for GTZAN and AudioSet datasets from GrahAM (b) t-SNE plots for 50% of the complex AudioSet data from (i) Siamese Network and (ii) GrahAM. (c) Illustration of the Siamese Training flow. (Introduction)Figure 2: (a) Visualization for the heatmaps generated from Grad-CAM method over example genere predictions from GrahAM. (b) Illustration of the Graph Network processing pipeline with reference to gradient taken from intermediate layers. (Our Approach)›

Related: TFIDF [1811.01287] Towards Sparse Hierarchical Graph Classifiers[1907.06119] Understanding Deep Learning Techniques for Image Segmentation[1907.06119v1] Understanding Deep Learning Techniques for Image Segmentation[1601.06615] A Taxonomy of Deep Convolutional Neural Nets for Computer Vision[1901.00596] A Comprehensive Survey on Graph Neural Networks[1901.00596v1] A Comprehensive Survey on Graph Neural Networks[1807.09975] Person Re-identification with Deep Similarity-Guided Graph Neural Network[1709.04396] A Tutorial on Deep Learning for Music Information Retrieval[1803.01164] The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches[1909.05667] Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations Related: Semantic Math [1811.01459] Deep Metric Learning by Online Soft Mining and Class-Aware Attention[1606.05400] Complex systems: features, similarity and connectivity[1412.8534] Disjunctive Normal Networks[1412.8534] Disjunctive Normal Networks[1805.08093] NeuralREG: An end-to-end approach to referring expression generation[1903.08923] Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition[1903.08923] Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition[1805.02339] A Hierarchical Matcher using Local Classifier Chains[1906.11248] Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data[1504.02366] A Collection of Challenging Optimization Problems in Science, Engineering and Economics

Related: TFIDF [1811.01287] Towards Sparse Hierarchical Graph Classifiers[1907.06119] Understanding Deep Learning Techniques for Image Segmentation[1907.06119v1] Understanding Deep Learning Techniques for Image Segmentation[1601.06615] A Taxonomy of Deep Convolutional Neural Nets for Computer Vision[1901.00596] A Comprehensive Survey on Graph Neural Networks[1901.00596v1] A Comprehensive Survey on Graph Neural Networks[1807.09975] Person Re-identification with Deep Similarity-Guided Graph Neural Network[1709.04396] A Tutorial on Deep Learning for Music Information Retrieval[1803.01164] The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches[1909.05667] Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations Related: Semantic Math [1811.01459] Deep Metric Learning by Online Soft Mining and Class-Aware Attention[1606.05400] Complex systems: features, similarity and connectivity[1412.8534] Disjunctive Normal Networks[1412.8534] Disjunctive Normal Networks[1805.08093] NeuralREG: An end-to-end approach to referring expression generation[1903.08923] Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition[1903.08923] Learning with Batch-wise Optimal Transport Loss for 3D Shape Recognition[1805.02339] A Hierarchical Matcher using Local Classifier Chains[1906.11248] Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data[1504.02366] A Collection of Challenging Optimization Problems in Science, Engineering and Economics