Open Access

Zhao Ren

• Automatically recognising audio signals plays a crucial role in the development of intelligent computer audition systems. Particularly, audio signal classification, which aims to predict a label for an audio wave, has promoted many real-life applications. Amounts of efforts have been made to develop effective audio signal classification systems in the real world. However, several challenges in deep learning techniques for audio signal classification remain to be addressed. For instance, training a deep neural network (DNN) from scratch is time-consuming to extracting high-level deep representations. Furthermore, DNNs have not been well explained to construct the trust between humans and machines, and facilitate developing realistic intelligent systems. Moreover, most DNNs are vulnerable to adversarial attacks, resulting in many misclassifications. To deal with these challenges, this thesis proposes and presents a set of deep-learning-based approaches for audio signal classification.Automatically recognising audio signals plays a crucial role in the development of intelligent computer audition systems. Particularly, audio signal classification, which aims to predict a label for an audio wave, has promoted many real-life applications. Amounts of efforts have been made to develop effective audio signal classification systems in the real world. However, several challenges in deep learning techniques for audio signal classification remain to be addressed. For instance, training a deep neural network (DNN) from scratch is time-consuming to extracting high-level deep representations. Furthermore, DNNs have not been well explained to construct the trust between humans and machines, and facilitate developing realistic intelligent systems. Moreover, most DNNs are vulnerable to adversarial attacks, resulting in many misclassifications. To deal with these challenges, this thesis proposes and presents a set of deep-learning-based approaches for audio signal classification. In particular, to tackle the challenge of extracting high-level deep representations, the transfer learning frameworks, benefiting from pre-trained models on large-scale image datasets, are introduced to produce effective deep spectrum representations. Furthermore, the attention mechanisms at both the frame level and the time-frequency level are proposed to explain the DNNs by respectively estimating the contributions of each frame and each time-frequency bin to the predictions. Likewise, the convolutional neural networks (CNNs) with an attention mechanism at the time-frequency level is extended to atrous CNNs with attention, aiming to explain the CNNs by visualising high-resolution attention tensors. Additionally, to interpret the CNNs evaluated on multi-device datasets, the atrous CNNs with attention are trained in the conditional training frameworks. Moreover, to improve the robustness of the DNNs against adversarial attacks, models are trained in the adversarial training frameworks. Besides, the transferability of adversarial attacks is enhanced by a lifelong learning framework. Finally, the experiments conducted with various datasets demonstrate that these presented approaches are effective to address the challenges.…