14. Array-based speech enhancement for microphones on seat belts
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Abstract
Microphones placed on the seat belts of an automobile (in the following called beltmics for short) are an interesting alternative compared to other sensor positions. Because of the small distance to the speaker’s mouth, usually a good signal quality (e. g., measured in terms of high signal-to-noise ratio) can be achieved. However, the signalsmay also be disturbed because beltmics can easily be touched by, e. g., clothes and they might be in the airstream of automotive ventilation systems. In addition, applying array processing techniques is difficult because the geometry is flexible and may change over time. This chapter presents signal enhancement algorithms designed for beltmics including echo cancellation, estimation of instationary noise, and microphone combining techniques. Results based on a real-time implementation are shown finally that demonstrate the performance of this new microphone type.
Abstract
Microphones placed on the seat belts of an automobile (in the following called beltmics for short) are an interesting alternative compared to other sensor positions. Because of the small distance to the speaker’s mouth, usually a good signal quality (e. g., measured in terms of high signal-to-noise ratio) can be achieved. However, the signalsmay also be disturbed because beltmics can easily be touched by, e. g., clothes and they might be in the airstream of automotive ventilation systems. In addition, applying array processing techniques is difficult because the geometry is flexible and may change over time. This chapter presents signal enhancement algorithms designed for beltmics including echo cancellation, estimation of instationary noise, and microphone combining techniques. Results based on a real-time implementation are shown finally that demonstrate the performance of this new microphone type.
Chapters in this book
- Frontmatter I
- Contents V
- Preface XIII
- List of contributing authors XIX
-
Part I: Vehicle System and Safety
- 1. Analysis of in-vehicle speech activity towards driver safety assessment 3
- 2. Stochastic behavior modeling for driver assistance using stream data processing 19
- 3. Using real road driving data to calibrate a model of front-end collision risk 37
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Part II: Driver Modeling
- 4. Driver mirror-checking action detection 55
- 5. Probabilistic driver modeling 77
- 6. Driving distance based analysis of driving maneuvers 99
- 7. Correlation of neurophysiological measurement of anxiety and driving behavior 111
- 8. Adaptation techniques for stochastic driver behavior modeling 123
- 9. Integrated modeling of driver gaze and vehicle operation behavior during lane changes 133
-
Part III: Signal Processing for HVI
- 10. Speaker activity detection for distributed microphone systems in cars 145
- 11. Speech enhancement employing feature domain reconstruction for robust in-vehicle speech recognition 161
- 12. Driver adaptive prediction for pedestrian detectability using in-vehicle camera images 171
- 13. An audio-visual in-car corpus “CENSREC-2-AV” for robust bimodal speech recognition 181
- 14. Array-based speech enhancement for microphones on seat belts 191
- 15. How to create a clean Lombard speech database using loudspeakers 209
- Index 227
Chapters in this book
- Frontmatter I
- Contents V
- Preface XIII
- List of contributing authors XIX
-
Part I: Vehicle System and Safety
- 1. Analysis of in-vehicle speech activity towards driver safety assessment 3
- 2. Stochastic behavior modeling for driver assistance using stream data processing 19
- 3. Using real road driving data to calibrate a model of front-end collision risk 37
-
Part II: Driver Modeling
- 4. Driver mirror-checking action detection 55
- 5. Probabilistic driver modeling 77
- 6. Driving distance based analysis of driving maneuvers 99
- 7. Correlation of neurophysiological measurement of anxiety and driving behavior 111
- 8. Adaptation techniques for stochastic driver behavior modeling 123
- 9. Integrated modeling of driver gaze and vehicle operation behavior during lane changes 133
-
Part III: Signal Processing for HVI
- 10. Speaker activity detection for distributed microphone systems in cars 145
- 11. Speech enhancement employing feature domain reconstruction for robust in-vehicle speech recognition 161
- 12. Driver adaptive prediction for pedestrian detectability using in-vehicle camera images 171
- 13. An audio-visual in-car corpus “CENSREC-2-AV” for robust bimodal speech recognition 181
- 14. Array-based speech enhancement for microphones on seat belts 191
- 15. How to create a clean Lombard speech database using loudspeakers 209
- Index 227
