Acoustic Scene Classification (ASC) is a task that classifies a scene according to environmental acoustic signals. Audios collected from different cities and devices often exhibit biases in feature distributions, which may negatively impact ASC performance. Taking the city and device of the audio collection as two types of data domain, this paper attempts to disentangle the audio features of each domain to remove the related feature biases. A dual-alignment framework is proposed to generalize the ASC system on new devices or cities, by aligning boundaries across domains and decision boundaries within each domain. During the alignment, the maximum classifier discrepancy and gradient reversed layer are used for the feature disentanglement of scene, city and device, while four candidate domain classifiers are proposed to explore the optimal solution of feature disentanglement. To evaluate the dual-alignment framework, three experiments of biased ASC tasks are designed: 1) cross-city ASC in new cities; 2) cross-device ASC in new devices; 3) cross-city-device ASC in new cities and new devices. Results demonstrate the superiority of the proposed framework, showcasing performance improvements of 0.9%, 19.8%, and 10.7% on classification accuracy, respectively. The effectiveness of the proposed feature disentanglement approach is further evaluated in both biased and unbiased ASC problems, and the results demonstrate that better-disentangled audio features can lead to a more robust ASC system across different devices and cities. This paper advocates for the integration of feature disentanglement in ASC systems to achieve more reliable performance.

Technologies and systems for indoor positioning, mapping, and navigation (IPMN) have rapidly developed over the latest decade due to advanced radio and light communications, the internet of things, intelligent and smart devices, big data, and so forth. Thus, a group of surveys for IPMN technologies, systems, standards, and solutions can be found in literature. However, currently there is no proposed solution that can satisfy all indoor application requirements; one of the biggest challenges is lack of standardization, even though several IPMN standards have been published by different standard developing organizations (SDOs). Therefore, this paper aims to re-survey indoor positioning and mapping technologies, in particular, the existing standards related to these technologies and to present guidance in the field. As part of our work, we provide an IPMN standards system architecture consisting of concepts, terms, models, indoor positioning technologies, software and tools, applications, services and policies, and indoor mapping and modelling; and, we present IPMN standards developed for our projects in practice, such as multi-source fusion positioning data interfaces; seamless cooperative positioning service interfaces; content model for indoor mapping and navigation, and specification for digital indoor map products.