This paper proposes innovative de-noise filters in a framework, whose aim is the localization of an acoustic source in a noisy environment. The main focuses are the automatic detection of transient sound events and the separation of the events of interest from the noise. A microphone array is used to capture timespatial information and an adaptive filter can be initialized to learn the ambient noise spectrum when signals of interest are absent. We propose an algorithm based on the Short Time Spectral Attenuation method to remove the noise from each sensor of the array, before the source localization task is performed. The Time Difference Of Arrival (TDOA) methods are used for multiple sources localization. The experimental results show the efficiency of our framework in stationary noisy environments.

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The growing demand for automatic surveillance systems that integrates different types of sensors, including microphones, requires to adapt and optimize the already studied techniques of Acoustic Source Localization to meet the constraints imposed by the new application scenario. In this paper, we present a real-time prototype for multiple acoustic sources localization in a far-filed and free-field environment. The prototype is composed by two linear arrays and utilizes an innovative approach for the localization of multiple sources. The algorithm is based on two steps: i) the separation of the sources by means of beamforming techniques and ii) the comparison of the power spectrum by means of a spectral distance measure. The prototype was successfully tested in a real environment.

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This paper describes the implementation of an innovative musical interface based on the sound localization capability of a microphone array. Our proposal is to allow a musician to plan and conduct the expressivity of a performance, by controlling in realtime an audio processing module through the spatial movement of a sound source, i.e. voice, traditional musical instruments, sounding mobile devices. The proposed interface is able to locate and track the sound in a two-dimensional space with accuracy, so that the x-y coordinates of the sound source can be used to control the processing parameters. In particular, the paper is focused on the localization and tracking of harmonic sound sources in real moderate reverberant and noisy environment. To this purpose, we designed a system based on adaptive parameterized Generalized Cross-Correlation (GCC) and Phase Transform (PHAT) weighting with Zero-Crossing Rate (ZCR) threshold, a Wiener filter to improve the Signal to Noise Ratio (SNR) and a Kalman filter to make the position estimation more robust and accurate. We developed a Max/MSP external objects to test the system in a real scenario and to validate its usability.

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