Deep Complex Convolutional Recurrent Network for Multi-Channel Speech Enhancement and Dereverberation

This paper proposes a neural network based system for multi-channel speech enhancement and dereverberation. Speech recorded indoors by a far field microphone, is invariably degraded by noise and reflections. Recent single channel enhancement systems have improved denoising performance, but do not reduce reverberation, which also reduces speech quality and intelligibility. To address this, we propose a deep complex convolution recurrent network (DCCRN) based multi-channel system, with integrated minimum power distortionless response (MPDR) beamformer and weighted prediction error (WPE) preprocessing. PESQ and STOI performance is evaluated on a test set of room impulse responses and noise samples recorded by the same setup. The proposed system shows a statistically significant improvement over competitive systems.acceptedVersio

On the Predictive Power of Objective Intelligibility Metrics for the Subjective Performance of Deep Complex Convolutional Recurrent Speech Enhancement Networks

Speech enhancement (SE) systems aim to improve the quality and intelligibility of degraded speech signals obtained from far-field microphones. Subjective evaluation of the intelligibility performance of these SE systems is uncommon. Instead, objective intelligibility measures (OIMs) are generally used to predict subjective performance increases. Many recent deep learning (DL) based SE systems, are expected to improve the intelligibility of degraded speech as measured by OIMs. However, validation of the ability of these OIMs to predict subjective intelligibility when enhancing a speech signal using DL-based systems is lacking. Therefore, in this study, we evaluate the predictive performance of five popular OIMs. We compare the metrics' predictions with subjective results. For this purpose, we recruited 50 human listeners, and subjectively tested both single channel and multi-channel Deep Complex Convolutional Recurrent Network (DCCRN) based speech enhancement systems. We found that none of the OIMs gave reliable predictions, and that all OIMs overestimated the intelligibility of ‘enhanced’ speech signals.acceptedVersio

Evaluation of Downlink IEEE802.16e Communication at Airports

Mobile WiMAX technology is proposed for ATS and AOC communications in airport areas. This technology provides a large amount of flexibility, incorporating optional use of advanced communication techniques and signal processing. Of particular importance is the use of multiple antenna techniques. In this paper the performance of Mobile WiMAX technology is assessed by means of simulations for communications over channel models suited for airport communications. The simulations include space time coding (STC) and spatial multiplexing (SM). The results illustrate the gain obtained using multiple antenna techniques in the case of non line-of-sight between transmitter and receiver, which may be exploited for increased cell size or increased throughput per cell. In addition, the effect of Weibull fading is illustrated for b-factors lower than 2. This leads to worse than Rayleigh fading, and should be taken into account when setting thresholds in the adaptive coding and modulation scheme.  Evaluation of Downlink IEEE802.16e Communication at Airport

Statistical Modelling for Estimation of OD Matrices for Public Transport Using Wi-Fi and APC Data

In this paper, statistical models are proposed to estimate trip level origin-destination (OD) matrices for public transport based on Wi-Fi data traffic. Wi-Fi monitoring equipment installed in 32 buses in Stavanger, Norway, collected Wi-Fi data during several months. The median received signal level of frames transmitted by a device and the time interval between the first and last frame are modelled as statistical distributions, conditional on whether the Wi-Fi device is on the bus or not. Based on these models and using passenger load data from Automatic Passenger Counting (APC) systems installed in the buses, the probability for each detected device being on or off the bus is estimated. When tested on large data sets, the proposed statistical method is more accurate than when hard thresholds for median received signal level and time interval of observation are applied.acceptedVersio

SECOMAS. Final report from the project Spectral Efficient Communications for Aeronautical Services (SECOMAS) - Technical

-SECOMAS was a research project running from January 2007 to December 2010 targeting spectral efficient communications for aeronautical services. The Research partners of the project were SINTEF and NTNU, and the project was funded by the Norwegian Research Council and members of the Norwegian ATM industry (AVINOR, Jotron, KDC, Thales Norway and NGPAS). This report contains three technical notes written during the project period and three conference papers. Together these notes and papers encompass the work done by SINTEF as part of SECOMAS. The work done by the Ph.D. student will be included in the Ph.D. report which is scheduled for June 2011. The topics covered by this report are: - An overview of current aeronautical CNS (Communications, Navigation, Surveillance) systems.- HEO satellite  communications for ATM in high latitudes- Airport Surface Datalink Communications (Appendix A)- MIMO techniques for air/ground communications (Appendix B)- MIMO techniques for aeronautical satellite communications (Appendix C)   Oppdragsgiver: Norwegian Research Counci

Deep Complex Convolutional Recurrent Network for Multi-Channel Speech Enhancement and Dereverberation

This paper proposes a neural network based system for multi-channel speech enhancement and dereverberation. Speech recorded indoors by a far field microphone, is invariably degraded by noise and reflections. Recent single channel enhancement systems have improved denoising performance, but do not reduce reverberation, which also reduces speech quality and intelligibility. To address this, we propose a deep complex convolution recurrent network (DCCRN) based multi-channel system, with integrated minimum power distortionless response (MPDR) beamformer and weighted prediction error (WPE) preprocessing. PESQ and STOI performance is evaluated on a test set of room impulse responses and noise samples recorded by the same setup. The proposed system shows a statistically significant improvement over competitive systems

Evaluation of downlink IEEE802.16e communication at airports

Mobile WiMAX technology is proposed for ATS and AOC communications in airport areas. This technology provides a large amount of flexibility, incorporating optional use of advanced communication techniques and signal processing. Of particular importance is the use of multiple antenna techniques. In this paper the performance of Mobile WiMAX technology is assessed by means of simulations for communications over channel models suited for airport communications. The simulations include space time coding (STC) and spatial multiplexing (SM). The results illustrate the gain obtained using multiple antenna techniques in the case of non line-of-sight between transmitter and receiver, which may be exploited for increased cell size or increased throughput per cell. In addition, the effect of Weibull fading is illustrated for b-factors lower than 2. This leads to worse than Rayleigh fading, and should be taken into account when setting thresholds in the adaptive coding and modulation scheme.  Evaluation of Downlink IEEE802.16e Communication at Airport

Measurement and modeling of the 5 GHz airport surface channel at Barajas Airport

AeroMACS is a system currently under development to be used for airport surface communications. It is based on the IEEE802.16-2009 standard, and is developed in cooperation with the WiMAX forum. The frequency band allocated to AeroMACS is 5091-5150 MHz. When specifying the AeroMACS system, knowledge of typical propagation conditions at airports is of importance. Typical path loss models are necessary to estimate the range of a transmitter within different zones of the airport, and typical fading characteristics are used to estimate the performance of e.g. the selected coding and modulation schemes

Deep Complex Convolutional Recurrent Network for Multi-Channel Speech Enhancement and Dereverberation

This paper proposes a neural network based system for multi-channel speech enhancement and dereverberation. Speech recorded indoors by a far field microphone, is invariably degraded by noise and reflections. Recent single channel enhancement systems have improved denoising performance, but do not reduce reverberation, which also reduces speech quality and intelligibility. To address this, we propose a deep complex convolution recurrent network (DCCRN) based multi-channel system, with integrated minimum power distortionless response (MPDR) beamformer and weighted prediction error (WPE) preprocessing. PESQ and STOI performance is evaluated on a test set of room impulse responses and noise samples recorded by the same setup. The proposed system shows a statistically significant improvement over competitive systems

Statistical Modelling for Estimation of OD Matrices for Public Transport Using Wi-Fi and APC Data

In this paper, statistical models are proposed to estimate trip level origin-destination (OD) matrices for public transport based on Wi-Fi data traffic. Wi-Fi monitoring equipment installed in 32 buses in Stavanger, Norway, collected Wi-Fi data during several months. The median received signal level of frames transmitted by a device and the time interval between the first and last frame are modelled as statistical distributions, conditional on whether the Wi-Fi device is on the bus or not. Based on these models and using passenger load data from Automatic Passenger Counting (APC) systems installed in the buses, the probability for each detected device being on or off the bus is estimated. When tested on large data sets, the proposed statistical method is more accurate than when hard thresholds for median received signal level and time interval of observation are applied.acceptedVersio