Title | ||
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End-to-End Signal-Aware Direction-of-Arrival Estimation Using Weighted Steered-Response Power |
Abstract | ||
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The direction-of-arrival (DOA) of acoustic sources is an important parameter used in multichannel acoustic signal processing to perform, e.g., source extraction. Deep learning-based time-frequency masking has been widely used to make DOA estimators signal-aware, i.e., to localize only the sources of interest (SOIs) and disregard other sources. The mask is applied to feature representations of the microphone signals. DOA estimators can either be model-based or deep learning-based, such that the combination with the deep learning-based masking estimator can either be hybrid or fully data-driven. Although fully data-driven systems can be trained end-to-end, existing training losses for hybrid systems like weighted steered-response power require ground-truth microphone signals, i.e., signals containing only the SOIs. In this work, we propose a loss function that enables training hybrid DOA estimation systems end-to-end using the noisy microphone signals and the ground-truth DOAs of the SOIs, and hence does not dependent on the ground-truth signals. We show that weighted steered-response power trained using the proposed loss performs on par with weighted steered-response power trained using an existing loss that depends on the ground-truth microphone signals. End-to-end training yields consistent performance irrespective of the explicit application of phase transform weighting. |
Year | Venue | Keywords |
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2022 | 2022 30th European Signal Processing Conference (EUSIPCO) | Direction-of-Arrival,Steered-Response Power,Deep Learning,Time-Frequency Masking,End-to-End |
DocType | ISSN | ISBN |
Conference | 2219-5491 | 978-1-6654-6799-5 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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Julian Wechsler | 1 | 0 | 0.34 |
Wolfgang Mack | 2 | 0 | 0.34 |
Emanuël A. P. Habets | 3 | 0 | 0.34 |