Smartwatch, a wrist-worn personal device, can be a mobile platform for information assistant services that provide useful information in daily life, such as context-aware reminders for elderly people. To realize such an assistant service, we have developed a mobile context logger (MCL) that recognizes the user's auditory environments and activities based on auditory and motion information derived from the user's smartwatch. The MCL uses two embedded deep neural network modules: an audio recognition module and a motion recognition module. The modules were trained with open datasets and embedded on a smartwatch to recognize the user's auditory environments and activities. In this paper, we describe an overview of the MCL and discuss the issues towards the mobile context-aware assistant service based on our development.

This paper presents a method to identify sound sources for structural monitoring, known as building health monitoring. This method allows to evaluate deterioration and damage of buildings by analyzing environmental sounds. The proposed method determines the location and features of sounds generated within a building, with its main characteristics being: (1) planar direction and height estimation; (2) visualization of sound features according to loudness, continuity, and pitch. The capabilities of the proposed building health monitoring method are verified using environmental sound data acquired at a building in Gunkanjima, which is a world heritage site from Japan.

The present paper addresses the problem of analyzing the work of forklift trucks. To solve the problem, a forklift truck working model based on a state transition model is constructed using human activity sensing data, where the key point is using forklift truck operating data to determine human intention. The novel contribution of the model is its use of data that express the intention of the forklift truck operator to categorize the work of the forklift truck. By using a graph model, the proposed method can be extended to analyzing the human characteristics of forklift truck work. With this technique, some features of the graph model can be used to distinguish the skill level of the forklift truck operator.

For understanding human activities in urban public spaces, we have been developing a sensing platform for visualizing data derived from sensor devices in the spaces. The platform collects sensing data from sensor devices installed in public spaces, and provide a simple communication interface to access the sensor data for client systems. In this demo abstract, we describe a working prototype of a sensor data streaming service for visualizing urban public spaces. The streaming data includes the number of pedestrians, pedestrian flows, and sound pressures of the human activities in the sensing area. As an application of the streaming service, we will show a client system that visualizes the current pedestrian flows in the sensing area with very little delay. The flows are visualized like a view of live camera without pedestrians' appearances. The system has a search interface to retrieve the sensing data in a past period and also visualizes them.

Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. The attractive features of the proposed algorithm are that the BD can be achieved by calculating the eigenvectors of a matrix and by using reference signals. However, the performance accuracy of the EVM depends highly on the computational result of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Simulation results will be presented for showing the validity of the proposed method.

The matrix inversion lemma gives an explicit formula of the inverse of a positive-definite matrix A added to a block of dyads (represented as BBH). It is well-known in the literature that this formula is very useful to develop a block-based recursive least-squares algorithm for the block-based recursive identification of linear systems or the design of adaptive filters. We already extended this result to the case when the matrix A is singular, and presented the matrix pseudo-inversion lemma. Such a singular case may occur in a situation where a given problem is overdetermined in the sense that it has more equations than unknowns. In this paper, based on these results, we propose a block-based adaptive multichannel super-exponential deflation algorithm. We present simulation results for the performance of the block-based algorithm in order to show the usefulness of the matrix pseudo-inversion lemma.

In this paper, we propose a system that can detect unusual sounds and directions by observing sound environment with microphone arrays. One of the attractive features of the system is to detect the unusual information through daily environmental sound measurement. Therefore the system does not require such troublesome processes that the detected sounds must be predefined, the predefined sounds must be collected, and using the collected sounds, their features must be modeled, where the conventional systems have such troublesomeness. Moreover, unlike conventional systems using video cameras, our system is not limited by video camera angles. A simple experimental result shows the validity of the proposed system.

Recently we develop two kinds of approaches, that is, the super-exponential method (SEM) and the eigenvector method (EVM), of which both can achieve the blind deconvolution for MIMO-IIR systems. It is shown that these methods are closely related each other. Based on this fact, we propose a new SEM incorporated with the EVM. Simulation results will be presented for showing the validity of the proposed method.

A particle filtering algorithm using the parameters in the EM (expectation-maximization) algorithm is proposed for tracking multiple sound sources. Differently from the conventional EM based algorithms, the proposed algorithm can track multiple sound sources without knowing their starting points. Moreover, an idea of the group tracking is applied to the particle filtering algorithm so that better tracking performances can be obtained. Experimental results show the validity of the proposed algorithm.

This paper presents eigenvector algorithms (EVAs) for blind deconvolution of multiple-input multiple-output infinite impulse response (MIMO-IIR) channels (convolutive mixtures). One of the attractive features of the proposed EVA is that it is insensitive to Gaussian noises which are added to the outputs of the channels; hence the proposed EVA is referred to as a "robust" eigenvector algorithm (REVA). Simulation results show the validity of the REVA.

This paper presents eigenvector algorithms (EVAs) for blind deconvolution (BD) of multiple-input multiple-output infinite impulse response (MIMO-IIR) channels (convolutive mixtures). Using the idea of reference signals, the EVA is derived. Differently from the conventional researches on EVAs, one of the novel points of the paper is that the EVA using any reference signal is applied to the BD problem of the MIMO-IIR system, and then the validity of the EVA is shown.

The matrix inversion lemma gives an explicit formula of the inverse of a positive-definite matrix A added to a block of dyads (represented as BBH) as follows: (A + BBH)-1= A-1- A-1B(I + BHA-1B)-1BHA-1. It is well-known in the literature that this formula is very useful to develop a block-based recursive least-squares algorithm for the block-based recursive identification of linear systems or the design of adaptive filters. We extend this result to the case when the matrix A is singular, and present a matrix pseudo-inversion lemma. Based on this result, we propose a block-based adaptive multi-channel super-exponential algorithm (BAMSEA). We present simulation results for the performance of the block-based algorithm in order to show the usefulness of the matrix pseudo-inversion lemma.

This letter presents an eigenvector algorithm (EVA) for blind deconvolution (BD) of multiple-input multiple-output infinite impulse response (MIMO-IIR) channels (convolutive mixtures), using the idea of reference signals. Differently from the conventional researches on EVAs, the proposed EVA utilizes only one reference signal for recovering all the source signals simultaneously. Computer simulations are presented for demonstrating the effectiveness of the proposed algorithm.

This paper presents an eigenvector algorithm (EVA) derived from a criterion using reference signals, in which the EVA is applied to the blind source separation (BSS) of instantaneous mixtures. The proposed EVA works such that source signals are simultaneously separated from their mixtures. This is a new result, which has not been clarified by the conventional researches. Simulation results show the validity of the proposed EVA

The so called "super-exponential" methods (SEMs) are attractive methods for solving multichannel blind deconvolution problem. The conventional SEMs, however, have such a drawback that they are very sensitive to Gaussian noise. To overcome this drawback, the robust super-exponential method (RSEM) were proposed for single-input single-output infinite impulse response (SISO-IIR) channels and for multi-input multi-output (MIMO) static channels (instantaneous mixtures). While the conventional SEMs use the second- and higher-order cumulants of observations, the RSEM uses only the higher-order cumulants of observations. Since higher-order cumulants are insensitive to Gaussian noise, the RSEM is robust to Gaussian noise. We proposed an RSEM extended to the case of MIMO-IIR channels (convolutive mixtures). To show the validity of the proposed RSEM, some simulation results are presented

This paper presents an eigenvector algorithm (EVA) derived from a criterion using reference signals, in which the EVA is applied to the blind source separation (BSS) of instantaneous mixtures. The proposed EVA works such that source signals are simultaneously separated from their mixtures. This is a new result, which has not been clarified by the conventional researches. Moreover, by modifying the criterion, the corresponding EVA which is robust to Gaussian noise is derived. Simulation results show the validity of the proposed EVAs

The so called "super-exponential" methods (SEMs) are attractive methods for solving multichannel blind deconvolution problem. The conventional SEMs, however, have such a drawback that they are very sensitive to Gaussian noise. To overcome this drawback, the robust super-exponential method (RSEM) was proposed for single-input single-output infinite impulse response (SISO-IIR) channels and for multi-input multi-output (MIMO) static channels (instantaneous mixtures). While the conventional SEMs use the second- and higher-order cumulants of observations, the RSEM uses only the higher-order cumulants of observations. Since higher-order cumulants are insensitive to Gaussian noise, the RSEM is robust to Gaussian noise. We proposed an RSEM extended to the case of MIMO-IIR channels (convolutive mixtures). To show the validity of the proposed RSEM, some simulation results are presented

The present paper deals with a signal separation technique, which can be applied, to the ears of robots. When we want a robot to recognize a desired signal in a situation where there are some noises (undesired signals), there exists such a case that the robot cannot recognize the desired signal. In this case, one must consider of separating the desired signal from the mixtures of it and the undesired signals. In the present paper, we propose a method in which such a separation problem can be solved. Experimental results show that our proposed method can solve successfully the signal separation problem.

The letter proposes a new iterative deflation algorithm to solve the blind source-factor separation for the outputs of multiple-input-multiple-output finite-impulse response (MIMO-FIR) channels driven by source signals that are temporally correlated but spatially uncorrelated. Using the proposed algorithm, filtered versions of the source signals, each of which is the contribution of each source signal to the outputs of MIMO-FIR channels, are extracted one by one from the mixtures of source signals.

In this paper, we propose a new iterative algorithm to solve the blind deconvolution problem of MIMO-FIR channels driven by source signals which are temporally second-order uncorrelated but fourth-order correlated and spatially second- and fourth-order uncorrelated. In our new approach, to solve the blind deconvolution problem, we consider two stages: First, filtered source signals are extracted from the mixtures of source signals. Second, the source signals are recovered from the filtered source signals.

This paper deals with blind separation that can extract original signals from their mixtures observed in a normal room. Our method achieves blind separation by making the mixed signals not correlating with each other. The validity of the proposed method has been confirmed by a computer simulation and an experiment in an anechoic room. In this paper, we apply our method to an experiment that extracts two source signals from their mixtures observed in a normal room.