In literature, particle velocity has been introduced to improve performance of spatial sound field reproduction systems. However, all existing work requires to have accurate particle velocity measurements at all of the discrete control points, which is difficult to obtain in real-world applications. In this work, we formulate continuous particle velocity expressions over space as a function of pressure coefficients in the modal domain that can be easily extracted by using a higher order microphone. The sound field within a target region is controlled by a weighted cost function we built to optimize the continuous particle velocity, as well as sound pressure, on the boundary of the region. In contrast to the conventional spatial sound field reproduction methods in the modal domain, the proposed method allows for non-uniform loudspeaker geometry with a limited number of loudspeakers, thus providing a flexible array arrangement. The performance of the proposed method is evaluated through numerical simulations in both a free field and a reverberant room. Finally, we prove the proposed method in an objective experiment with real-world measurements of room impulse response.

Sound intensity is a fundamental quantity describing acoustic wave fields and it contains both energy and directivity information. It is used in a variety of applications such as source localization, reproduction, and power measurement. Until now, intensity is defined at a point in space, however given sound propagates over space, knowing its spatial distribution could be more powerful. This paper formulates spatial sound intensity vectors in spherical harmonic domain such that the vectors contain energy and directivity information over continuous spatial regions. These representations are derived with finite sets of closed form coefficients enabling ease of implementation.

Sound intensity is an acoustic quantity closely linked with human perception of sound location, and it can be controlled to create a high level of realism to humans in soundfield reproduction systems. In this paper, we present an intensity matching technique to optimally reproduce sound intensity over a continuous spatial region using an irregular loudspeaker array. This avoids several known limitations in the previous works on intensity based soundfield reproduction, such as a single sweet spot for the listener and a regular loudspeaker geometry that is difficult to implement in real-world applications. In contrast to the previous works, the new technique uses a cost function we built to optimize sound intensity over space by exploiting spatial sound intensity distributions. The spatial sound intensity distribution is represented by spherical harmonic coefficients of sound pressure, which are widely used to describe a spatial soundfield. Compared to the conventional spatial soundfield reproduction method of pressure matching in the spherical harmonic domain and the HOA max-rE decoding method optimizing sound intensity at a single position, we show that the intensity matching technique has better overall performance with two different irregular loudspeaker layouts through simulations. The impact of microphone noise on reproduction performance is also assessed. Finally, we carry out perceptual localization experiments to validate the proposed method.

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With the acceleration of the process of “smart tourism”, the development of tourist attractions is increasingly inseparable from the support of tourist big data. In the management practice of scenic spots, the big data of tourists has become an important basis for scenic spot managers to make timely decisions. This article analyzes the real-time significance of tourist big data from the relevant perspective of the Information Theory. In terms of evidence, this article takes the Redriver Canyon Rafting scenic spot as a case to investigate its information construction, and expounds the contribution of big data in its economic benefits. Empirically, this article uses regression analysis, causal model and other methods to study the data before and after the information construction of scenic spots, and discusses the important value of real-time big data of tourists for the safety management of tourist scenic spots.