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A binaural sound sources localisation application for smart phones
The ability to estimate positions of sound sources is one that gives animals a 360° awareness of their acoustic environment. This helps compliment the visual scene which is restricted to 180° in humans. Unfortunately, deaf people are left out on this ability. Smart phones are rapidly becoming a comm...
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| Format: | Thesis |
| Language: | English |
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Department of Electrical Engineering
2017
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| Summary: | The ability to estimate positions of sound sources is one that gives animals a 360° awareness of their acoustic environment. This helps compliment the visual scene which is restricted to 180° in humans. Unfortunately, deaf people are left out on this ability. Smart phones are rapidly becoming a common tool amongst mobile users in developed and emerging markets. Their processing ability has more than doubled since their introduction to mass consumer markets by Apple in 2007. Top-end smart phones such as the Samsung Galaxy Series; 3, 4, and 5 models, have two microphones with which one can acquire stereo recordings. The purpose of this research project was to establish a feasible Sound source localization algorithm for current top-end smart phones, and to recommend hardware improvements for future smart phones, to pave way for the use of smart phones as advanced auditory sensory devices capable of acting as avatars for intelligent remote systems to learn about different acoustic scenes with help of human users. The GCC-PHAT algorithm was chosen as the underlying core DOA algorithm due to its suitability for pair-wise localization as highlighted in literature. A stochastic power accumulation algorithm was designed and implemented to improve estimation outcomes by GCC-PHAT. This algorithm was based on inspiration from W-disjoint orthogonality assumption in literature and was extended to perform sound source counting and time domain source separation. The system yielded satisfactory azimuth estimates of sound source directions in real time with pin-point DOA estimation accuracy rates of 64%, and 90.67% accuracy rate when a tolerance of ± 1 correlation sample is considered. An effort to resolve front back ambiguity using phone orientation data from the MEMs sensors yielded un-satisfactory results prompting a recommendation that an extra microphone would be needed to achieve 360° localization in a more user friendly way. The dissertation concludes with plans for further work on the topic and provision of a further refined API and optimised libraries to facilitate development of customised solutions using this system. |
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