The social implementation of small autonomous mobile robots is advancing in delivery and accompanying services. However, inappropriate waiting positions can obstruct pedestrian flow and impair facility operations. Conventional waiting location estimation methods require long-term observation of actual pedestrian walking history, resulting in high introduction costs and environmental dependence. This research proposes a fluid simulation-based method that estimates waiting locations using only floor maps without prior observation. By approximating pedestrian flow as a two-dimensional incompressible fluid, the method extracts waiting candidates from velocity fields determined by environmental geometry. Through comparative verification with agent-based simulation and human subject experiments, we confirmed that the proposed method achieves estimation accuracy equivalent to history-based approaches while requiring no prior observation. The method demonstrates environmental adaptability across simple corridors to complex spaces, with a practical threshold of 0.4 times maximum flow velocity effectively distinguishing appropriate waiting locations. The approach enables rapid deployment and adaptation to layout changes, addressing key challenges in mobile robot social implementation.

We propose a novel application of a family of non-parametric statistical models to estimate head-related transfer functions (HRTFs) using spatial-temporal Gaussian processes (GPs). In this approach, we model the head-related impulse response (HRIR) utilizing non-parametric regression via a GP. The challenge posed by this problem involves accurate modeling of the spatial correlation structure jointly with the temporal correlation structure at each spatial location for the HRIR. We solve this problem by constructing a joint spatial-temporal kernel characterizing the GP regression model. To perform inference, we estimate the hyper-parameters of the GP regression kernel via maximum signal-to-deviation-ratio estimation on the basis of a real experimental setup in which we collected observations of the HRIR using two head-and-torso simulators (HATSs): KEMAR and B&K. We also perform cross validation of the model by training on the KEMAR system and assessing the generalization of our model and its out-of-sample predictive power for HRIRs at any locations that we predict by the model assessed on the B&K system. The corresponding HRTFs are obtained as the Fourier transform of the HRIRs. In the experiments, we show that our method is robust against variation in the azimuth interval needed to perform high-accuracy interpolation and has the expressive power to handle the individual characteristics of each HATS.

A single-channel dereverberation method with low computational complexity is proposed. We introduce the complementary Wiener filter which can suppress a late reverberation during silence intervals via theoretical analysis and numerical calculation. An implementation represents reductions both of memory consumption and operative calculations compared to a conventional method; each reduction is almost by half. Dereverberation performance is evaluated by an experimental simulation using speech signals and measured room impulse responses. The performance under several hundreds of msec of the reverberation time is similar to the conventional method: 6 [dB] reverberation reduction and 3 [dB] improvement of target-to-interference ratio.

A method for estimating the sound source depth, i.e., the distance between a source and receiver, using a small-size array is proposed. The proposed method uses the spatial distribution pattern of quasi-independent signal components obtained by the frequency-domain independent component analysis (FDICA) as the cue for depth estimation. The quasi-independent components are calculated by applying FDICA to array signals with very high redundancy, for example, 60 microphone signals for a pair of sources; therefore, signal components associated with reflection signals are obtained even though they are correlated with the direct signal. Experimental evaluation using a small-size microphone array with a large number of elements confirms that the average (RMS) estimation error of the proposed method is 0.33 m, which is sufficiently accurate for our applications.

A new noise reduction method based on spatio-temporal frequency analysis is proposed that can be applied to head-related impulse response (HRIR), which is an impulse response between the sound source and the ear canal entrance. HRIR measurement is some times conducted in spaces which are not free fields, such as sound proof chambers with low reverberation. Conventional noise reduction methods such as synchronous averaging need multiple measurements under an identical measurement condition. However, taking many measurements requires much time, and subjects must be patient. Therefore, a noise reduction method which requires fewer measurements is needed. In our proposed method, noises are sup pressed using a two-dimensional filter that is designed in the spatio temporal frequency domain. Two experiments were conducted to improve the signal-to-noise ratio (SNR). The maximum SNR improvement achieved 1.28 dB, and the HRIRs measured in high reverberation were also improved, indicating that the proposed method effectively improved HRIRs without multiple measurements.

A sound source separation method based on frequency-domain independent component analysis (FD-ICA) is proposed. This method fully utilizes the dodecahedral microphone array (DHMA), which has several merits: 1) the size of the array is very small and thus easy to handle; 2) the amplitude difference among microphones on the different surfaces is large; and 3) it is less affected by spatial aliasing in the higher frequency region. In the proposed method, in order to solve the permutation problem in FD-ICA through clustering acoustic transfer functions, amplitude and phase differences are optimally combined as a function of frequency. A DHMA of 8 cm in diameter with 60 microphones is used for the experiment, where up to twelve sound sources (speech/musical instruments) are separated using the proposed algorithm. The separation performance of the proposed method attains 24 dB in the signal-to-interference ratio (SIR) improvement score for the case of twelve sources. Since the performance is better by up to 10 dB in comparison to the conventional method, our results confirm the effectiveness of the proposed method.

With the increased presence and recent advances of drive recorders, rich driving data that include video, vehicle acceleration signals, driver speech, GPS data, and several sensor signals can be continuously recorded and stored. These advances enable researchers to study driving behavior more extensively for traffic safety. However, increasing the variety and the amount of driving data complicates the simultaneous browsing of various data and finding desired data from large databases. In this study, we develop a browsing and retrieval system for driving data that provides a multi-modal data browser, query- and similarity-based retrieval functions, and a fast browsing function that skips redundant scenes. For sharing data with several users, this system can be used via networks from PCs or smartphones, This system uses a time-series active search, which has been successfully used for fast search of audio and video data, as its retrieval function algorithm. In a few seconds, this system can retrieve driving scenes that are similar to an input scene from 80,000 scenes. Retrieval performance was compared in various retrieval conditions by changing the codebook size of the vector quantization for the histogram features and a combination of driving signals. Experimental results showed that more than 97% retrieval performance was achieved for driving behaviors of left/right turns and curves using a combination of such complementary information as steering angles and lateral acceleration. We also compared the proposed method to a conventional image-based retrieval method using subjective similarity scores of driving scenes. Our proposed system retrieved similar scenes with about a 75% retrieval performance that was five points higher than a conventional image-based retrieval method. It is because image-based method is sensitive to changes of image in the area except in the region of interest for driving data retrieval. The fast browsing function also skipped scenes that could not be skipped by an image-based method.

ABSTRACT A sound field reproduction method which uses blind source separation and head-related transfer function is proposed. In the proposed system, multichannel acoustic signals captured at the distant microphones are encoded to a set of location/signal pairs of virtual sound sources based on frequency-domain ICA. After estimating the locations and the signals of the virtual sources, by convolving the controlled acoustic transfer functions with each signal, the spatial sound at the selected point is constructed. In the evaluation, the sound field made by 6 sound sources is captured using 48 distant microphones and is encoded into set of virtual sound sources. Subjective evaluation shows that there is no significant difference between natural and reconstructed sound when more than 6 virtual sources are used. Therefore the effectiveness of the encoding algorithm as well as the virtual source representation is confirmed.

We propose an integrative recognition method of speech accompanied with gestures such as pointing. Simultaneously generated speech and pointing complementarily help the recognition of both, and thus the integration of these multiple modalities may improve recognition performance. As an example of such multimodal speech, we selected the explanation of a geometry problem. While the problem was being solved, speech and fingertip movements were recorded with a close-talking microphone and a 3D position sensor. To find the correspondence between utterance and gestures, we propose probability distribution of the time gap between the starting times of an utterance and gestures. We also propose an integrative recognition method using this distribution. We obtained approximately 3-point improvement for both speech and fingertip movement recognition performance with this method.

This paper describes new equipment for measuring head-related transfer functions (HRTFs) near a listener's head. 3D sounds in headphones are generated by the convolution of sound signals and an HRTF, which is defined as the acoustical transfer function between a point sound source and the entrance to the ear canal. A loudspeaker is usually used for HRTF measurements, and a distance of more than 1 m separates the loudspeaker and the subject. The region within 1 m of the head is called the 'proximal region,' where a small loudspeaker is needed for accurately measuring HRTF, that is, a conventional loudspeaker cannot be used. In our study, a micro-dodecahedral loudspeaker with twelve piezoelectric ceramic devices is used for HRTF measurements at a diameter is 38 mm. Our experiments examined the characteristics of this loudspeaker. From the results, our developed loudspeaker provides similar performance to a point source, and it is very effective for measuring the HRTFs in the proximal region.

Abstract Speech recognition should be as an eyes-free and hands-free interface. To realise this technology, we need to clar-ify acoustics in a helmet and determine how much high-level riding noise affects captured speech data. This pa-per describes the acoustics in a helmet and transfer func-tions of the microphone position. We constructed a datacollection system and collected the speech data of motor-bike riders on city roads and express highways. Speechrecognition experiments were conducted and we obtaineda recognition rate high of 83.1%. 1. Introduction Riding a motorbike requires more care than driving a car.Even when a rider idles his/her motorbike, such as whenhe/she waits at a red light, button operations are incon-venient because the rider needs to remove his/her glovesto push the buttons. Therefore, for motorbike riders, aneyes-free and hands-free interface is required for operat-ing information appliances, such as a cellular phone anda route navigation system. Thus, speech recognition is avery important technology.This study investigated the feasibility of speechrecognition for motorbike riders. On a motorbike, rid-ers are exposed directly to high-level noises such as windnoise, engine noise, and road noise. It is known thatexposed noise level is varied by various factors such asspeed, riding position, and helmets[1, 2]. In order to re-alize speech recognition on a motorbike, we first need toinvestigate how much such factors degrade conventionalspeech recognition performance.Riders must put on a helmet when they ride motor-bikes. Helmets are designed to reduce noise level, how-ever, we need to clarify how much this reduction con-tributes to speech recognition using microphones insidethe side of helmets. Moreover, we need to investigateacoustics in a helmet, because a helmet has a very smallcavity.In this paper, we measured acoustics in a helmetto determine microphone positions for collecting riders’speech corpus. We then collected the speech data utteredby motorbike riders riding on a highway. We also providean analysis of the corpus and the results of our speechrecognition evaluation.