Conveners
A09.01/A17.01 Machine learning for array processing: S054
- Maximo Cobos (Universitat de València)
- Jose J. Lopez (Universitat Politecnica de Valencia)
A09.01/A17.01 Machine learning for array processing: P520
- Maximo Cobos (Universitat de València)
- Thushara Abhayapala
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Siavash Zaid (Technische Universität Berlin)10/09/2026, 16:20A09 Machine learning and artificial intelligence in acoustics
Sound Source Characterization (SSC) with microphone arrays has seen significant advances through deep learning. However, most data-driven methods are trained on data from a single, fixed microphone array geometry, making the resulting models geometry-specific and difficult to transfer to other configurations. Moreover, many architectures do not explicitly use sensor position information. To...
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Thomas Deppisch (Chalmers University of Technology)10/09/2026, 16:40A09 Machine learning and artificial intelligence in acoustics
Multichannel speech enhancement is widely used as a front-end in microphone array processing systems. While most existing approaches produce a single enhanced signal, direction-preserving multiple-input multiple-output (MIMO) methods instead aim to provide enhanced multichannel signals that retain directional properties, enabling downstream applications such as beamforming, binaural rendering,...
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Adam Kujawski (Technische Universität Berlin)10/09/2026, 17:00A09 Machine learning and artificial intelligence in acoustics
Covariance Matrix Fitting estimates acoustic source strengths on a predefined focus grid by fitting a modeled microphone Cross-Spectral Matrix (CSM) to the sample CSM and is often formulated as a nonnegative LASSO problem. Classical algorithms for the LASSO need model selection and many iterations for accurate reconstructions. Algorithm unfolding reduces this effort by learning selected...
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Samuel A. Verburg (Technical University of Denmark)10/09/2026, 17:20A09 Machine learning and artificial intelligence in acoustics
Elementary wave models are at the core of microphone array processing and estimation problems in acoustics. Wave models are often formulated in the frequency domain, and the problems solved one frequency at a time. On the other hand, time-domain formulations are often more suitable, as they preserve the signals' spatio-temporal structure (e.g., temporal sparsity of wavefronts), and can utilize...
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Clara Luzón Álvarez (Universitat de València)10/09/2026, 17:40A09 Machine learning and artificial intelligence in acoustics
Anomalous sound detection (ASD) is a key task in industrial applications, where reliable detection of machine faults can prevent costly downtime and ensure operational safety. Although single-channel recordings simplify data acquisition and model design, they often do not capture spatial information that can be critical for distinguishing between normal and anomalous acoustic events in complex...
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Felix Holzmüller (Institute of Electronic Music and Acoustics)11/09/2026, 09:00A09 Machine learning and artificial intelligence in acoustics
Adaptive local active noise control (ANC) requires accurate estimates of the sound pressure at the point of cancellation. These are often obtained using nearby microphones via virtual sensing methods such as the remote microphone technique (RMT). Obs-TasNet was recently proposed as a neural network–based method for online estimation of RMT observation filter coefficients. In this work, we...
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