Evolution of Artificial Soundscape in a Natural Environment Norihiro Maruyama 1 , Itsuki Doi 1 , Atsushi Masumori 1 , Mizuki Oka 2 , Takashi Ikegami 1 , Victoria Vesna 3 , Charles Taylor 4 1 Graduate School of Arts and Sciences, The University of Tokyo, Komaba, Tokyo 153-8902 Japan 2 Department of Computer Science, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8577, Japan 3 Department of Design and Media Arts, UCLA, United States, 4 Department of Ecology and Evolutionary Biology, UCLA, United States ikeg@sacral.c.u-tokyo.ac.jp Introduction Research on artificial life involves attempts to produce life- like phenomena through simulations using computer mod- els, robotics, and biochemistry. In this paper, we propose a new approach to artificial life experimentation in an open environment, with an autonomous sensor network (ASN) we have developed (Maruyama et al., 2013). It takes a form of an experimental sound generative art installation (Ikegami et al., 2012), aiming to explore behavior over a longer term in an open environment including living systems, e.g., song birds. Our main study principle for installing autonomy in a sys- tem or an environment, a concept that has been fostered in the field of artificial life. Autonomy in a system creates a pleasant distance between object and observer but also arouses emotions which, like those we have for our pets, can give rise to long-lasting relationships. Based on this concept, we have created a sound instal- lation using an ASN. The system obtains sensor informa- tion from the environment, maintains a basic (artificial) metabolism, and changes its behavior after a certain amount of information processing has occurred (e.g., light and hu- midity sensor data). Those information will be used to con- trol the pattern and amplitude of the parametric speakers. By coupling the ASN driven soundscape with a natural sound- scape, we are also proposing a new idea of how an artificial system can resonate with a particular environmental pattern including real living systems. We believe such interaction between artificial and real systems will provide a new exper- imental platform for studying and understanding open natu- ral systems. ASN system We propose an ASN system that is spatially distributed in the real world (Maruyama et al., 2013; Ikegami et al., 2012). One node is composed of sensors (e.g. light and humidity sensors), that senses the corresponding environment infor- mation with an adaptive sensing cycle. The sensor informa- tion obtained by each node will be sent to other nodes (we set the number of nodes at two) via wireless connections. In other words, each sensor is attached to a buffer of each node that accumulates sensor information from its own sen- sor. Two kinds of buffers (one associated with light, and the other with humidity) are associated with each node. This system is unique in that we use a metaphor of spa- tially extended chemical reaction schema. A modified Gray- Scott reaction-diffusion model is used as a design for this sensor network. This model is a translation of a spatially ex- tended chemical reaction into an active sensing and wireless network system. The comparison is summarized in Table 1. Table 1: Comparison between Chemical and Sensor Net- works Chemical network Sensor network chemical species sensor type chemical sensors digital sensors chemical reaction tank digital sensor buffer diffusion packet switching Sensory data in each unit are put onto the buffer, and an assumed reaction will take place in that buffer. Suppose that sensor values A and B are received by the corresponding sensor. For example, we use the reaction A + B 2 → C to change the sensing cycle of the sensor, whose reaction speed is proportional to [A][B] 2 . The sensing cycle length is defined as how often a sensor receives the sensory value from the environment. Namely, the sensing cycle will be increased or decreased, proportional to the reaction rate. It should be noted that the sensor values will not be affected by the reaction but, only the cycle length will be updated. After computing the reaction rates, those sensor values will be sent to the other wirelessly connected sensor nodes. As a result, the sensing data will be circulating in the network through a wireless connection. Fig.1 illustrates the rough circuit of Arduino, XBee, and the main processor that implements the virtual chemical net- work in one unit, which has four inputs from and four out- puts to other sensor nodes connected by a wireless connec-