Investigation and Evaluation of Adaptive Algorithms for Multichannel Active Noise Control System

This dissertation focuses on the investigation and evaluation of adptive algorithms for multichannel active noise control system. The aim of the research is to investigate the effectiveness of the FxLMS algorithm and the pre-trained control filter in attenuating various types of noise. The study begins with a comprehensive review of the existing literature on active noise control, highlighting the significance of noise reduction in different applications. The theoretical foundations of the FxLMS algorithm and the pre-trained control filter are then presented, including their underlying principles and mathematical formulations. Through a comprehensive analysis, a clear understanding of these methods is established. To assess the performance of the FxLMS algorithm and the pre-trained control filter, extensive simulation experiments are conducted using real-world noise signals. The experiments include scenarios such as aircraft noise, traffic noise, and mixed noise. The results of the simulations are used to compare the noise reduction capabilities of the two methods and to evaluate their effectiveness under different noise conditions. The findings indicate that the FxLMS algorithm exhibits remarkable noise reduction performance. It demonstrates a strong ability to track and respond quickly to varying noise patterns. In the initial stages of noise reduction, the pre-trained control filter shows better performance. However, as time progresses, the FxLMS algorithm consistently achieves higher average noise reduction levels compared to the pre-trained control filter. Additionally, the FxLMS algorithm shows faster responsiveness during transitional periods when noise characteristics change. These findings contribute to the field of noise control and provide valuable insights for designing efficient noise reduction systems.

Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation

This paper addresses semi-supervised semantic segmentation by exploiting a small set of images with pixel-level annotations (strong supervisions) and a large set of images with only image-level annotations (weak supervisions). Most existing approaches aim to generate accurate pixel-level labels from weak supervisions. However, we observe that those generated labels still inevitably contain noisy labels. Motivated by this observation, we present a novel perspective and formulate this task as a problem of learning with pixel-level label noise. Existing noisy label methods, nevertheless, mainly aim at image-level tasks, which can not capture the relationship between neighboring labels in one image. Therefore, we propose a graph based label noise detection and correction framework to deal with pixel-level noisy labels. In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss. Then, we adopt a superpixel-based graph to represent the relations of spatial adjacency and semantic similarity between pixels in one image. Finally we correct the noisy labels using a Graph Attention Network (GAT) supervised by detected clean labels. We comprehensively conduct experiments on PASCAL VOC 2012, PASCAL-Context and MS-COCO datasets. The experimental results show that our proposed semi supervised method achieves the state-of-the-art performances and even outperforms the fully-supervised models on PASCAL VOC 2012 and MS-COCO datasets in some cases.

Mining Objects: Fully Unsupervised Object Discovery and Localization From a Single Image

The goal of our work is to discover dominant objects without using any annotations. We focus on performing unsupervised object discovery and localization in a strictly general setting where only a single image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Mining (OM), which exploits the ad-vantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically,Object Mining first converts the feature maps from a pre-trained CNN model into a set of transactions, and then frequent patterns are discovered from transaction data base through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions,typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful pat-terns in an unsupervised manner. Extensive experiments on a variety of benchmarks demonstrate that Object Mining achieves competitive performance compared with the state-of-the-art methods.

Unsupervised Part Mining for Fine-grained Image Classification

Fine-grained image classification remains challenging due to the large intra-class variance and small inter-class variance. Since the subtle visual differences are only in local regions of discriminative parts among subcategories, part localization is a key issue for fine-grained image classification. Most existing approaches localize object or parts in an image with object or part annotations, which are expensive and labor-consuming. To tackle this issue, we propose a fully unsupervised part mining (UPM) approach to localize the discriminative parts without even image-level annotations, which largely improves the fine-grained classification performance. We first utilize pattern mining techniques to discover frequent patterns, i.e., co-occurrence highlighted regions, in the feature maps extracted from a pre-trained convolutional neural network (CNN) model. Inspired by the fact that these relevant meaningful patterns typically hold appearance and spatial consistency, we then cluster the mined regions to obtain the cluster centers and the discriminative parts surrounding the cluster centers are generated. Importantly, any annotations and sophisticated training procedures are not used in our proposed part localization approach. Finally, a multi-stream classification network is built for aggregating the original, object-level and part-level features simultaneously. Compared with other state-of-the-art approaches, our UPM approach achieves the competitive performance.