Joerg Schmalenstroeer

Online Diarization of Streaming Audio-Visual Data for Smart Environments

For an environment to be perceived as being smart, contextual information has to be gathered to adapt the systems behavior and its interface towards the user. Being a rich source of context information speech can be acquired unobtrusively by microphone arrays and then processed to extract information about the user and his environment. In this paper, a system for joint temporal segmentation, speaker localization, and identification is presented, which is supported by face identification from video data obtained from a steerable camera. Special attention is paid to latency aspects and online processing capabilities, as they are important for the application under investigation, namely ambient communication. It describes the vision of terminal-less, session-less and multi-modal telecommunication with remote partners, where the user can move freely within his home while the communication follows him. The speaker diarization serves as a context source, which has been integrated in a service-oriented middleware architecture and provided to the application to select the most appropriate I/O device and to steer the camera towards the speaker during ambient communication.

Smartphone-based sensor fusion for improved vehicular navigation

In this paper we present a system for car navigation by fusing sensor data on an Android smartphone. The key idea is to use both the internal sensors of the smartphone (e.g., gyroscope) and sensor data from the car (e.g., speed information) to support navigation via GPS. To this end we employ a CAN-Bus-to-Bluetooth adapter to establish a wireless connection between the smartphone and the CAN-Bus of the car. On the smartphone a strapdown algorithm and an error-state Kalman filter are used to fuse the different sensor data streams. The experimental results show that the system is able to maintain higher positioning accuracy during GPS dropouts, thus improving the availability and reliability, compared to GPS-only solutions.

A combined hardware-software approach for acoustic sensor network synchronization

In this paper we present an approach for synchronizing a wireless acoustic sensor network using a two-stage procedure. First the clock frequency and phase differences between pairs of nodes are estimated employing a two-way message exchange protocol. The estimates are further improved in a Kalman filter with a dedicated observation error model. In the second stage network-wide synchronization is achieved by means of a gossiping algorithm which estimates the average clock frequency and phase of the sensor nodes. These averages are viewed as frequency and phase of a virtual master clock, to which the clocks of the sensor nodes have to be adjusted. The amount of adjustment is computed in a specific control loop. While these steps are done in software, the actual sampling rate correction is carried out in hardware by using an adjustable frequency synthesizer. Experimental results obtained from hardware devices and software simulations of large scale networks are presented. HighlightsAcoustic sensor network synchronization for speaker position estimation.State estimation using Kalman filter and GMM error model.Gossiping algorithm for synchronization of large sensor networks.

DOA-based microphone array postion self-calibration using circular statistics

In this paper we propose an approach to retrieve the absolute geometry of an acoustic sensor network, consisting of spatially distributed microphone arrays, from reverberant speech input. The calibration relies on direction of arrival measurements of the individual arrays. The proposed calibration algorithm is derived from a maximum-likelihood approach employing circular statistics. Since a sensor node consists of a microphone array with known intra-array geometry, we are able to obtain an absolute geometry estimate, including angles and distances. Simulation results demonstrate the effectiveness of the approach.

A Gossiping Approach to Sampling Clock Synchronization in Wireless Acoustic Sensor Networks

In this paper we present an approach for synchronizing the sampling clocks of distributed microphones over a wireless network. The proposed system uses a two stage procedure. It first employs a two-way message exchange algorithm to estimate the clock phase and frequency difference between two nodes and then uses a gossiping algorithm to estimate a virtual master clock, to which all sensor nodes synchronize. Simulation results are presented for networks of different topology and size, showing the effectiveness of our approach.

Amigo Context Management Service with Applications in Ambient Communication Scenarios

The Amigo Context Management Service (CMS) provides an open infrastructure for the exchange of contextual information between context sources and context clients. Whereas context sources supply context information, retrieved from sensors or services within the networked home environment, context clients utilize those information to become context-aware.

Aligning training modelswith smartphone properties in WiFi fingerprinting based indoor localization

We are concerned with the so-called fingerprinting method for WiFi-based indoor positioning, where the measured received signal strength index (RSSI) is compared with training data to come up with an estimate of the users location. We introduce a method for adapting the trained models to the statistics of the RSSI values of the target (testing) WiFi device, which is derived from the Maximum Likelihood Linear Regression (MLLR) framework. By introducing regression classes the assumption of a linear relationship between the RSSI readings of the testing device and the training data is relaxed, leading to superior adaptation performance. Parameter adaptation formulas are derived for the general case of censored and dropped data. While censoring occurs due to the limited sensitivity of WiFi chips, dropping is probably caused by limitations of the operating system of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms.

Server based indoor navigation using RSSI and inertial sensor information

In this paper we present a system for indoor navigation based on received signal strength index information of Wireless-LAN access points and relative position estimates. The relative position information is gathered from inertial smartphone sensors using a step detection and an orientation estimate. Our map data is hosted on a server employing a map renderer and a SQL database. The database includes a complete multilevel office building, within which the user can navigate. During navigation, the client retrieves the position estimate from the server, together with the corresponding map tiles to visualize the users position on the smartphone display.

Multi-stage coherence drift based sampling rate synchronization for acoustic beamforming

Multi-channel speech enhancement algorithms rely on a synchronous sampling of the microphone signals. This, however, cannot always be guaranteed, especially if the sensors are distributed in an environment. To avoid performance degradation the sampling rate offset needs to be estimated and compensated for. In this contribution we extend the recently proposed coherence drift based method in two important directions. First, the increasing phase shift in the short-time Fourier transform domain is estimated from the coherence drift in a Matched Filter-like fashion, where intermediate estimates are weighted by their instantaneous SNR. Second, an observed bias is removed by iterating between offset estimation and compensation by resampling a couple of times. The effectiveness of the proposed method is demonstrated by speech recognition results on the output of a beamformer with and without sampling rate offset compensation between the input channels. We compare MVDR and maximum-SNR beamformers in reverberant environments and further show that both benefit from a novel phase normalization, which we also propose in this contribution.

Investigations into Bluetooth low energy localization precision limits

In this paper we study the influence of directional radio patterns of Bluetooth low energy (BLE) beacons on smartphone localization accuracy and beacon network planning. A two-dimensional model of the power emission characteristic is derived from measurements of the radiation pattern of BLE beacons carried out in an RF chamber. The Cramer-Rao lower bound (CRLB) for position estimation is then derived for this directional power emission model. With this lower bound on the RMS positioning error the coverage of different beacon network configurations can be evaluated. For near-optimal network planing an evolutionary optimization algorithm for finding the best beacon placement is presented.