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 encodes sound-field parameters that allow for the spatio-temporal reconstruction within the measurement area at bandwidths where static methods would require impractical setups. An existing framework considers such basis functions from a signal processing point of view. It uses sinc-function based interpolation filters which are highly localized around sampled trajectories and may lead to ill-posed problems unless sparsity constraints are made, especially for locations that are away from microphone trajectories. In this paper, we present a new physical interpretation of the dynamic sampling problem. Transferring the problem into frequency domain, we describe samples of a moving microphone in terms of sampled spherical harmonic functions. The use of these global basis functions leads to dynamic measurements that inherently encode expanded sound-field information and, thus, allow for robust reconstruction at off-trajectory positions.

In this article, a new convex formulation for the acoustic channel-equalization problem is proposed and efficient ways for solving it are presented. Both the alternating direction method of multipliers and a proximal algorithm are studied for optimization. Time-domain and frequency-domain constraints are included in an equal way, allowing for simultaneous reverberation reduction and frequency-response equalization. Rather than aiming at full channel equalization, we follow the channel-reshaping paradigm proposed in earlier works and try to push the reverberation tail under the temporal masking limit of the human auditory system, thus making reverberation inaudible for human listeners. While the algorithm is presented for the multiple-input-multiple-output (MIMO) scenario, the single-channel case is included as a special case. Comparisons with methods from the literature for the single- and multi-channel cases show that the new algorithm has faster convergence than the known ones. The proximal algorithm even allows for solving extremely large problems with very low memory demand.

The perceived quality of a sound played in a closed room is often degraded by the added reverberation. In order to combat these effects, the methods of room impulse response equalization may be used. Typically, properties of the human auditory system, such as temporal masking, are used for a better control of the late echoes. There, a prefilter is used to modify the played signal, which renders the echoes inaudible for a given position. When targeting a human listener who is always performing small movement, the method fails due to the spatial mismatch. In such a case, a whole volume needs to be equalized, which requires a huge amount of measurements. In this work, we propose to reduce this burden by measuring only a random subset of the required room impulse responses and reconstruct a grid employing sparse methods. By further interpolation an equalization of the whole target area is achieved.

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 calibration and spatial positioning of a large number of microphones. Dynamic sound-field measurements involving tracked microphones may weaken this spatial sampling problem. However, for aliasing-free reconstruction, there is still the need of sampling a huge number of unknown sound-field variables. Thus in real-world applications, the trajectories may be expected to lead to underdetermined sampling problems. In this paper, we present a compressed sensing framework that allows for stable and robust sub-Nyquist sampling of sound fields by use of moving microphones.

The conventional sampling of sound fields by use of stationary microphones is impractical for large bandwidths. Satisfying the Nyquist-Shannon sampling theorem in three-dimensional space requires a huge number of sampling positions. Dynamic sound-field measurements with moving microphones together with a compressed-sensing recovery allow for weakening the spatial sampling problem. For bandlimited signals, the dynamic samples taken along the microphone trajectory may be related to the room impulse responses on a virtual grid in space via spatial interpolation. The tracking of the microphone positions and the knowledge of the excitation sequence allow for setting up a linear system of equations that can be solved for the room impulse responses on the modeled virtual grid. Nevertheless, there is still the necessity for recovering a huge number of sound-field variables, in order to ensure aliasing-free reconstruction. Thus, for practical applications, random or suboptimally chosen trajectories may be expected to lead to underdetermined sampling problems for a given volume of interest. In this paper, we present a compressed sensing framework that enables us to uniquely solve the dynamic sampling problem despite having underdetermined variables. The spatio-temporal sampling problem is integrated into compressed sensing models that allow for stable and robust sub-Nyquist sampling given incoherent measurements. For a modeled equidistant grid and sparse Fourier representations, the influence of the microphone trajectories on the compressed sensing problem is investigated and a simple expression is derived for evaluating trajectories with regard to compressed-sensing based recovery.

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 comes along with further difficulties such as exact microphone positioning and calibration of multiple microphones. In this paper, we present a method for measuring sound fields using moving microphones whose trajectories are known to the algorithm. At that, the number of microphones is customizable by trading measurement effort against sampling time. Through spatial interpolation of the dynamic measurements, a system of linear equations is set up which allows for the reconstruction of the entire sound field inside the volume of interest.

We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks. An audio scene is firstly transformed into a sequence of high-level label tree embedding feature vectors. The vector sequence is then divided into multiple subsequences on which a deep GRU-based recurrent neural network is trained for sequence-to-label classification. The global predicted label for the entire sequence is finally obtained via aggregation of subsequence classification outputs. We will show that our approach obtains an F1-score of 97.7% on the LITIS Rouen dataset, which is the largest dataset publicly available for the task. Compared to the best previously reported result on the dataset, our approach is able to reduce the relative classification error by 35.3%.