Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling

Abstract

This technical report describes the IOA team's submission for TASK1A of DCASE2019 challenge. Our acoustic scene classification (ASC) system adopts a data augmentation scheme employing generative adversary networks. Two major classifiers, 1D deep convolutional neural network integrated with scalogram features and 2D fully convolutional neural network integrated with Mel filter bank features, are deployed in the scheme. Other approaches, such as adversary city adaptation, temporal module based on discrete cosine transform and hybrid architectures, have been developed for further fusion. The results of our experiments indicates that the final fusion systems A-D could achieve an accuracy higher than 85% on the officially provided fold 1 evaluation dataset.

References in corpus (3)

• Conditional Image Synthesis With Auxiliary Classifier GANs
• Deep Scattering Spectrum
• A multi-device dataset for urban acoustic scene classification

Cited by in corpus (14)

• PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
• Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks
• A Two-Stage Approach to Device-Robust Acoustic Scene Classification
• A Squeeze-and-Excitation and Transformer based Cross-task System for Environmental Sound Recognition
• Acoustic Scene Classification with Spectrogram Processing Strategies
• Low-Complexity Models for Acoustic Scene Classification Based on Receptive Field Regularization and Frequency Damping
• Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization
• Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification
• ACGAN-based Data Augmentation Integrated with Long-term Scalogram for Acoustic Scene Classification
• A Curated Dataset of Urban Scenes for Audio-Visual Scene Analysis
• DcaseNet: An integrated pretrained deep neural network for detecting and classifying acoustic scenes and events
• Robust Acoustic Domain Identification with its Application to Speaker Diarization
• An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances
• DD-CNN: Depthwise Disout Convolutional Neural Network for Low-complexity Acoustic Scene Classification