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We report an experiment with an acoustic surveillance system comprised of a computer and microphone situated in a typical office environment. The system continuously analyzes the acoustic activity at the recording site, separates all interesting events, and stores them in a database. All interesting acoustic events over a duration of more than two months were recorded. A number of low-level signal features are computed from the audio signal and used to classify and identify sound events. The analysis reveals interesting patterns and activities which would be difficult to find by any other means.

Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore's law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.

Syllables are elementary building blocks of bird song. In sounds of many songbirds a large class of syllables can be approximated as amplitude and frequency varying brief sinusoidal pulses. In this article we test how well bird species can be recognized by comparing simple sinusoidal representations of isolated syllables. Results are encouraging and show that with limited sets of bird species a recognizer based on this signal model may already be sufficient.