The localization of a speaker inside a closed environment is often approached by real-time processing of multiple audio signals captured by a set of microphones. One of the leading related methods for sound source localization, the steered-response power (SRP), searches for the point of maximum power over a spatial grid. High-accuracy localization calls for a dense grid and/or many microphones, which tends to impractically increase computational requirements. This paper proposes a new method for sound source localization (called H-SRP), which applies the SRP approach to space regions instead of grid points. This arrangement makes room for the use of a hierarchical search inspired by the branch-and-bound paradigm, which is guaranteed to find the global maximum in anechoic environments and shown experimentally to also work under reverberant conditions. Besides benefiting from the improved robustness of volume-wise search over point-wise search as to reverberation effects, the H-SRP attains high performance with manageable complexity. In particular, an experiment using a 16-microphone array in a typical presentation room yielded localization errors of the order of 7 cm, and for a given fixed complexity, competing methods' errors are two to three times larger.Index Terms-Sound source localization, steered-response power, microphone array, computational complexity, hierarchical search, branch-and-bound.

Self-localization of smart portable devices serves as foundation for several novel applications. This work proposes a set of algorithms that enable a mobile device to passively determine its position relative to a known reference with centimeter precision, based exclusively on the capture of acoustic signals emitted by controlled sources around it. The proposed techniques tackle typical practical issues such as reverberation, unknown speed of sound, line-of-sight obstruction, clock skew, and the need for asynchronous operation. After their theoretical developments and off-line simulations, the methods are assessed as real-time applications embedded into off-the-shelf mobile devices operating in real scenarios. When line of sight is available, position estimation errors are at most 4 cm using recorded signals. Index Terms-Acoustic sensor localization, least-squares, time of flight, time-difference of flight ! D. B. Haddad is with the ). Some preliminary results of this work appeared in [1], [2].1. Acoustic equivalent of multipath propagation effect, wellknown in electromagnetic-based wireless communications.

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This paper deals with the localization of acoustic sensors based on signals emitted by loudspeakers at known positions. In particular, a model for distortions in time-of-flight (TOF) estimates applicable to the sensor localization problem is presented along with closed-form solutions with low computational cost. The proposed techniques are able to approximate the sensor position even when the TOFs are corrupted by an unknown delay, there is a sampling frequency mismatch between the A/D and D/A converters associated with sensor and loudspeakers, and the speed of sound is unknown. Simulations and an experiment on real data demonstrate that the proposed methods are able to estimate sensor positions with less than 2 cm of error in the evaluated scenarios.

The wide availability of mobile devices with embedded microphones opens up opportunities for new applications based on acoustic sensor localization (ASL). Among them, this paper highlights mobile device self-localization relying exclusively on acoustic signals, but with previous knowledge of reference signals and source positions. The problem of finding the sensor position is stated as a function of estimated times-of-flight (TOFs) or time-differences-of-flight (TDOFs) from the sound sources to the target microphone, and the main practical issues involved in TOF estimation are discussed. Least-squares ASL solutions are introduced, followed by other strategies inspired by sound source localization solutions: steered-response power, which improves localization accuracy, and a new region-based search, which alleviates complexity. A set of complementary techniques for further improvement of TOF/TDOF estimates are reviewed: sliding windows, matching pursuit, and TOF selection. The paper proceeds with proposing a novel ASL method that combines most of the previous material, whose performance is assessed in a real-world example: in a typical lecture room, the method achieves accuracy better than 20 cm.