Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
A Monaural Cue Sound Localizer
Analog Integrated Circuits and Signal Processing
Combined Monaural and Binaural Localization of Sound Sources
ASILOMAR '95 Proceedings of the 29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set)
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Sound Systems: Design and Optimization: Modern Techniques and Tools for Sound System Design and Alignment
Automatic Configuration Recognition Methods in Modular Robots
International Journal of Robotics Research
High performance 3D sound localization for surveillance applications
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Binaural Tracking of Multiple Moving Sources
IEEE Transactions on Audio, Speech, and Language Processing
Shooter localization in wireless microphone networks
EURASIP Journal on Advances in Signal Processing - Special issue on microphone array speech processing
Robotic orientation towards speaker for human-robot interaction
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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We consider the problem of estimating the incident angle of a sound, using only a single microphone. The ability to perform monaural (single-ear) localization is important to many animals; indeed, monaural cues are also the primary method by which humans decide if a sound comes from the front or back, as well as estimate its elevation. Such monaural localization is made possible by the structure of the pinna (outer ear), which modifies sound in a way that is dependent on its incident angle. In this paper, we propose a machine learning approach to monaural localization, using only a single microphone and an "artificial pinna" (that distorts sound in a direction-dependent way). Our approach models the typical distribution of natural and artificial sounds, as well as the direction-dependent changes to sounds induced by the pinna. Our experimental results also show that the algorithm is able to fairly accurately localize a wide range of sounds, such as human speech, dog barking, waterfall, thunder, and so on. In contrast to microphone arrays, this approach also offers the potential of significantly more compact, as well as lower cost and power, devices for sounds localization.