In this paper, we have proposed a low-cost self-localization method which uses 4 elements of microphones, wheel rotation and sound sources as beacons, whose absolute location and frequency bands are known. The proposed method consists of following 4 steps. The proposed method (i) execute self-localization using wheel-based odometry, (ii) estimate direction-of-arrival (DOA) of the sound sources using sounds recorded by the elements of the microphone array, (iii) predict the DOA of the sound sources from estimated location and pose, and (iv) conduct self-localization by integrating all of the information. To evaluate the proposed method, experiments were conducted. The proposed method was compared to the conventional methods, which were wheel-based odometry and self-localization using only DOA. In the experiments, we have supposed the house-cleaning robot and its trajectory. As results, without any obstacles or walls, the mean of the estimation errors by wheel-based odometry were 670 mm and 0.08 rad, and those of self-localization using only DOA were 2870 m and 0.07 rad in the worst case. In contrast with these methods, proposed method results in 69 mm, 0.02 rad as the worst estimation error of self location and pose. From the result with occlusion of a sound source, the mean of the localization error increased 60 mm, as the proposed method detects the incorrect DOA and prevents it from estimation. From the result with reflective wave from wall, there was a place where the localization error was large. The cause of this error was considered as directivity of sound source. These results indicate that the proposed method is feasible under indoor environment.

In this study, we evaluated a method of estimating the contact force of a bone-conducted sound transducer using human subjects. The method was previously proposed and evaluated only with a human model. First, the relationship between the contact force and the electrical impedance was validated for 12 human subjects from 10 Hz to 60 kHz. The results showed that the electrical impedance shows four peaks and that the peaks change with contact force for all subjects in the same manner. A method of estimating the contact force was implemented with a three-layered neural network and evaluated with the data from 12 human subjects. The estimation results showed that 90% of the estimation error was within ±0.43 N, which shows that the estimation of contact force is possible. This result enables the estimation of contact force only from the electrical impedance and may support reproducible fitting of the bone-conducted sound transducer.

In this paper, a robust indoor localization method using microphone pairs and asynchronous acoustic beacons was proposed. The proposed method is applicable even with a two-channel microphone pair, which is the minimal configuration of a microphone array. The proposed method estimates location by using the cross-correlation functions of the measured signals as location likelihoods. Three experiments were conducted to evaluate the proposed method. Four beacons were located at the corners of a localizing area of 4 m by 4 m and emitted signals with a bandwidth of 2 kHz. The localization results were compared to the previous method with deterministic direction-of-arrival estimation. The 90th percentiles of the localization error were 0.23 m for the proposed method with two microphones, 0.19 m for the proposed method with four microphones, and 0.30 m for the previous method under conditions without significant reverberation. Under a condition with reflective walls, the 90th percentile of the localization error of the previous method increased to 0.49 m, while that of the proposed method was only increased to 0.23 m for two microphones and 0.19 m for four microphones. The proposed method contributes to a robust localization in indoor environments and relieves the constraints of receiver configuration.INDEX TERMS Acoustic beacons, cross correlation, indoor localization, microphone pairs, particle filter.

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Noninvasive bone-conducted hearing aids require the consistent fitting of transducers for consistent hearing. In this paper, a method of estimating the contact force and amount of hair between a person’s skin and a bone-conducted sound transducer using electrical impedance is proposed. Experiments are conducted with a human surface model consisting of hair, skin, and bone. The estimator is implemented with a three-layered neural network. Ten measurements for 70 conditions are conducted by changing the contact force from 0 to 5 N and the amount of hair from 0 to 169.6 mm3. With the trained estimator, it is possible to estimate contact force and the intermediate material thickness with mean errors of 0.025 N and 0.424 mm3. This result supports the feasibility of the proposed method and contributes to the reproducible placement of the bone-conducted sound transducer.

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