科研成果

The digital economy has become a driving force for global economic development, resulting in high demand for balanced regional development. Using surname distance as a proxy variable for cultural distance, this study examined the impact of cultural differences on the development of a regional digital economy. The results of the analysis of panel data from 31 Chinese provinces from 2011 to 2019 indicated that the development of a region's digital economy positively contributes to the development of the digital economy in areas of cultural proximity. Further analysis of the mechanisms of cultural differences in the digital economy showed that cultural distance affects the development of the digital economy in a province through three mechanisms: birth rate, divorce rate, and the share of small families. Moreover, the findings suggest regional, divorce, and demographic heterogeneity in the impact of cultural distance on the digital economy.

Conventional room geometry blind inference techniques with acoustic signals often rely on prior knowledge, such as source signals or source positions, limiting their applicability when the sound source is unknown. To solve this problem, the authors propose a novel multitask deep neural network (DNN) model that jointly estimates sound source localization and room geometry using signals captured by a spherical microphone array. Considering the coupling between sound source content and environmental parameters in reverberation signals, extracted early reflection direction and delay information as network inputs to estimate spatial parameters is used, ensuring independence from the sound source signal. The proposed model employs a hierarchical architecturewith dedicated subnetworks to process direction-of-arrival (DOA) andtime-difference-of-arrival features, followed by a shared fusion module that exploits geometricconstraints between source and boundary positions. Compared with traditional methods, thismodel requires less prior environmental information and performs sound source localizationand room geometry inference with single-position sound field measurements. Experimentalresults from simulations and real measurements demonstrate the method’s effectiveness andprecision compared with conventional approaches across various scenarios.

Existing methods utilizing spatial information for sound source separation require prior knowledge of the direction of arrival (DOA) of the source or utilize estimated but imprecise localization results, which impairs the separation performance, especially when the sound sources are moving. In fact, sound source localization and separation are interconnected problems, that is, sound source localization facilitates sound separation while sound separation contributes to refined source localization. This paper proposes a method utilizing the mutual facilitation mechanism between sound source localization and separation for moving sources. The proposed method comprises three stages. The first stage is initial tracking, which tracks each sound source from the audio mixture based on the source signal envelope estimation. These tracking results may lack sufficient accuracy. The second stage involves mutual facilitation: Sound separation is conducted using preliminary sound source tracking results. Subsequently, sound source tracking is performed on the separated signals, thereby refining the tracking precision. The refined trajectories further improve separation performance. This mutual facilitation process can be iterated multiple times. In the third stage, a neural beamformer estimates precise single-channel separation results based on the refined tracking trajectories and multi-channel separation outputs. Simulation experiments conducted under reverberant conditions and with moving sound sources demonstrate that the proposed method can achieve more accurate separation based on refined tracking results.