Abstract
Sound source proximity and distance estimation are of great interest in many practical applications, since they provide significant information for acoustic scene analysis. As both tasks share complementary qualities, ensuring efficient interaction between these two is crucial for a complete picture of an aural environment. In this paper, we aim to investigate several ways of performing joint proximity and direction estimation from binaural recordings, both defined as coarse classification problems based on Deep Neural Networks (DNNs). Considering the limitations of binaural audio, we propose two methods of splitting the sphere into angular areas in order to obtain a set of directional classes. For each method we study different model types to acquire information about the direction-of-arrival (DoA). Finally, we propose various ways of combining the proximity and direction estimation problems into a joint task providing temporal information about the onsets and offsets of the appearing sources. Experiments are performed for a synthetic reverberant binaural dataset consisting of up to two overlapping sound events.
Original language | English |
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Title of host publication | 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) |
Publisher | IEEE |
Pages | 331-335 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-5871-6 |
DOIs | |
Publication status | Published - 2020 |
Publication type | A4 Article in conference proceedings |
Event | IEEE International Conference on Human-Machine Systems - Virtual, Rome, Italy Duration: 7 Sept 2020 → 9 Sept 2020 |
Conference
Conference | IEEE International Conference on Human-Machine Systems |
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Country/Territory | Italy |
City | Virtual, Rome |
Period | 7/09/20 → 9/09/20 |
Keywords
- Deep learning
- Image analysis
- Direction-of-arrival estimation
- Conferences
- Estimation
- Signal processing
- Multitasking
- binaural audio
- binaural localization
- distance estimation
Publication forum classification
- Publication forum level 1