10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems
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Anne Theiß
Abstract
The communication inside a vehicle can be impaired by background noise while driving at medium or higher velocities and by the unusual orientation of the passengers as they are not facing each other. As a consequence, the passengers often start to raise their voices and to change their positions by leaning forward or, in case of the front passengers, turning backwards to improve this situation. These adaptations to the communication situation may be uncomfortable, exhausting, and might result in an increased risk for accidents if the driver turns around (and does not look on the street). An in-car communication system (ICC system) can improve the communication by recording the speech signals and reproducing the enhanced signals over the loudspeakers inside the compartment, which are close to the listening passengers. After and during the design and the development of such systems, the achieved quality is of great interest. Therefore, a spectral distance approach is presented in this chapter. Even if spectral distance measures are well established and often used for quality evaluation in speech communication systems, we face here a special challenge, which is the creation of an appropriate reference signal. In addition, an investigation of the reliability of the proposed quality estimate by means of a subjective listening test was performed and the results are presented.
Abstract
The communication inside a vehicle can be impaired by background noise while driving at medium or higher velocities and by the unusual orientation of the passengers as they are not facing each other. As a consequence, the passengers often start to raise their voices and to change their positions by leaning forward or, in case of the front passengers, turning backwards to improve this situation. These adaptations to the communication situation may be uncomfortable, exhausting, and might result in an increased risk for accidents if the driver turns around (and does not look on the street). An in-car communication system (ICC system) can improve the communication by recording the speech signals and reproducing the enhanced signals over the loudspeakers inside the compartment, which are close to the listening passengers. After and during the design and the development of such systems, the achieved quality is of great interest. Therefore, a spectral distance approach is presented in this chapter. Even if spectral distance measures are well established and often used for quality evaluation in speech communication systems, we face here a special challenge, which is the creation of an appropriate reference signal. In addition, an investigation of the reliability of the proposed quality estimate by means of a subjective listening test was performed and the results are presented.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Contributing Authors VII
- Introduction XI
-
Part A: Driver/Vehicle Interaction Systems
- 1 MobileUTDrive: A Portable Device Platform for In-vehicle Driving Data Collection 3
- 2 Semantic Analysis of Driver Behavior by Data Fusion 25
- 3 Predicting When Drivers Need AR Guidance 35
- 4 Driver’s Mental Workload Estimation with Involuntary Eye Movement 49
- 5 Neurophysiological Driver Behavior Analysis 67
- 6 Modeling the Relationship between Driver Gaze Behavior and Traffic Context during Lane Changes Using a Recurrent Neural Network 87
- 7 A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition 101
- 8 Head Pose as an Indicator of Drivers’ Visual Attention 113
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Part B: Models & Theories of Driver/Vehicle Systems
- 9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking 135
- 10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems 149
- 11 Combination of Hands-Free and ICC Systems 165
- 12 Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants 183
- 13 In-Car Communication: From Single- to Four-Channel with the Frequency Domain Adaptive Kalman Filter 213
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Part C: Self–driving and the Mobility in 2050
- 14 The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 233
- 15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints 251
- 16 Connected and Automated Vehicles: Study of Platooning 263
- 17 Epilogue – Future Mobility 2050 285
- Index 311
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Contributing Authors VII
- Introduction XI
-
Part A: Driver/Vehicle Interaction Systems
- 1 MobileUTDrive: A Portable Device Platform for In-vehicle Driving Data Collection 3
- 2 Semantic Analysis of Driver Behavior by Data Fusion 25
- 3 Predicting When Drivers Need AR Guidance 35
- 4 Driver’s Mental Workload Estimation with Involuntary Eye Movement 49
- 5 Neurophysiological Driver Behavior Analysis 67
- 6 Modeling the Relationship between Driver Gaze Behavior and Traffic Context during Lane Changes Using a Recurrent Neural Network 87
- 7 A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition 101
- 8 Head Pose as an Indicator of Drivers’ Visual Attention 113
-
Part B: Models & Theories of Driver/Vehicle Systems
- 9 Evolving Neural Network Controllers for Tractor-Trailer Vehicle Backward Path Tracking 135
- 10 Spectral Distance Analysis for Quality Estimation of In-Car Communication Systems 149
- 11 Combination of Hands-Free and ICC Systems 165
- 12 Insights into Automotive Noise PSD Estimation Based on Multiplicative Constants 183
- 13 In-Car Communication: From Single- to Four-Channel with the Frequency Domain Adaptive Kalman Filter 213
-
Part C: Self–driving and the Mobility in 2050
- 14 The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 233
- 15 Vehicle Ego-Localization with a Monocular Camera Using Epipolar Geometry Constraints 251
- 16 Connected and Automated Vehicles: Study of Platooning 263
- 17 Epilogue – Future Mobility 2050 285
- Index 311
