Towards Informative Path Planning for Acoustic SLAM

Acoustic scene mapping is a challenging task as microphone arrays can often localize sound sources only in terms of their directions. Spatial diversity can be exploited constructively to infer source-sensor range when using microphone arrays installed on moving platforms, such as robots. As the absolute location of a moving robot is often unknown in practice, Acoustic Simultaneous Localization And Mapping (a-SLAM) is required in order to localize the moving robot’s positions and jointly map the sound sources. Using a novel a-SLAM approach, this paper investigates the impact of the choice of robot paths on source mapping accuracy. Simulation results demonstrate that a-SLAM performance can be improved by informatively planning robot paths

Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers

Acoustic scene mapping creates a representation of positions of audio sources such as talkers within the surrounding environment of a microphone array. By allowing the array to move, the acoustic scene can be explored in order to improve the map. Furthermore, the spatial diversity of the kinematic array allows for estimation of the source-sensor distance in scenarios where source directions of arrival are measured. As sound source localization is performed relative to the array position, mapping of acoustic sources requires knowledge of the absolute position of the microphone array in the room. If the array is moving, its absolute position is unknown in practice. Hence, Simultaneous Localization and Mapping (SLAM) is required in order to localize the microphone array position and map the surrounding sound sources. In realistic environments, microphone arrays receive a convolutive mixture of direct-path speech signals, noise and reflections due to reverberation. A key challenge of Acoustic SLAM (a-SLAM) is robustness against reverberant clutter measurements and missing source detections. This paper proposes a novel bearing-only a-SLAM approach using a Single-Cluster Probability Hypothesis Density filter. Results demonstrate convergence to accurate estimates of the array trajectory and source positions

Does TV Col Have the longest Recorded Positive Superhumps?

Re-examination of extensive photometric data of TV Col reveals evidence for a permanent positive superhump. Its period (6.4 h) is 16 percent longer than the orbital period and obeys the well known relation between superhump period excess and binary period. At 5.5-h, TV Col has an orbital period longer than any known superhumping cataclysmic variable and, therefore, a mass ratio which might be outside the range at which superhumps can occur according to the current theory. We suggest several solutions for this problem.Comment: 5 pages, 2 eps. figures, Latex, proceedings of `Evolution of Binary and Multiple Star Systems', a Meeting in Celebration of Peter Eggleton's 60th Birthday, Bormio, Italy, ASP Conference Series, eds. Ph. Podsiadlowski et al., ASP, San Francisc

Discriminative feature domains for reverberant acoustic environments

Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment iden- tification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Ma- chine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Er- ror Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Ex- perimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications