6 accepted papers
Continuously moving microphones produce a high number of spatially dense sound-field samples with low effort in hardware and acquisition time. By interpreting the dynamic procedure as the non-uniform sampling of spatial basis functions, a system of linear equations can be set up. Its solution encode…
For many applications of hand gesture recognition, a delay-free, affordable, and mobile system relying on body signals is mandatory. Therefore, we propose an approach for hand gestures classification given signals of inertial measurement units (IMUs) that works with extremely short windows to avoid…
For conventional sampling of sound-fields, the measurement in space by use of stationary microphones is impractical for high audio frequencies. Satisfying the Nyquist-Shannon sampling theorem requires a huge number of sampling points and entails other difficulties, such as the need for exact calibra…
We present in this work an approach for audio scene classification. Firstly, given the label set of the scenes, a label tree is automatically constructed where the labels are grouped into meta-classes. This category taxonomy is then used in the feature extraction step in which an audio scene instanc…
The sampling of sound fields involves the measurement of spatially dependent room impulse responses, where the Nyquist-Shannon sampling theorem applies in both the temporal and spatial domains. Therefore, sampling inside a volume of interest requires a huge number of sampling points in space, which…
We introduce a new learned descriptor for audio signals which is efficient for event representation. The entries of the descriptor are produced by evaluating a set of regressors on the input signal. The regressors are class-specific and trained using the random regression forests framework. Given an…