Josef Kulmer

Phase estimation in single-channel speech enhancement: limits-potential

In this paper, we present an overview on the previous and recent methods proposed to estimate a clean spectral phase from a noisy observation in the context of single-channel speech enhancement. The importance of phase estimation in speech enhancement is inspired by the recent reports on its usefulness in finding a phase-sensitive amplitude estimation. We present a comparative study of the recent phase estimation methods and elaborate their limits. We propose a new phase enhancement method relying on phase decomposition and time-frequency smoothing filters. We demonstrate that the proposed time-frequency phase smoothing method successfully reduces the variance of the noisy phase at harmonics. Our results on different speech and noise databases and different signal-to-noise ratios show that in contrast to the existing benchmark methods only the proposed method balances a tradeoff between a joint improvement in perceived quality of 0.2 in PESQ score and speech intelligibility of 2% by phase-only enhancement.

Phase Estimation in Single Channel Speech Enhancement Using Phase Decomposition

Conventional speech enhancement methods typically utilize the noisy phase spectrum for signal reconstruction. This letter presents a novel method to estimate the clean speech phase spectrum, given the noisy speech observation in single-channel speech enhancement. The proposed method relies on the phase decomposition of the instantaneous noisy phase spectrum followed by temporal smoothing in order to reduce the large variance of noisy phase, and consequently reconstructs an enhanced instantaneous phase spectrum for signal reconstruction. The effectiveness of the proposed method is evaluated in two ways: phase enhancement-only and by quantifying the additional improvement on top of the conventional amplitude enhancement scheme where noisy phase is often used in signal reconstruction. The instrumental metrics predict a consistent improvement in perceived speech quality and speech intelligibility when the noisy phase is enhanced using the proposed phase estimation method.

Harmonic phase estimation in single-channel speech enhancement using phase decomposition and SNR information

In conventional single-channel speech enhancement, typically the noisy spectral amplitude is modified while the noisy phase is used to reconstruct the enhanced signal. Several recent attempts have shown the effectiveness of utilizing an improved spectral phase for phase-aware speech enhancement and consequently its positive impact on the perceived speech quality. In this paper, we present a harmonic phase estimation method relying on fundamental frequency and signal-to-noise ratio (SNR) information estimated from noisy speech. The proposed method relies on SNR-based time-frequency smoothing of the unwrapped phase obtained from the decomposition of the noisy phase. To incorporate the uncertainty in the estimated phase due to unreliable voicing decision and SNR estimate, we propose a binary hypothesis test assuming speech-present and speech-absent classes representing high and low SNRs. The effectiveness of the proposed phase estimation method is evaluated for both phase-only enhancement of noisy speech and in combination with an amplitude-only enhancement scheme. We show that by enhancing the noisy phase both perceived speech quality as well as speech intelligibility are improved as predicted by the instrumental metrics and justified by subjective listening tests.

A probabilistic approach for phase estimation in single-channel speech enhancement using von mises phase priors

In many artificial intelligence systems human voice is considered as the medium for information transmission. Human-machine communication by voice becomes difficult when speech is mixed with some background noise. As a remedy, a single-channel speech enhancement is indispensable for reducing background noise from noisy speech to make it suitable for automatic speech recognition and telephony speech. While the conventional techniques for single-channel speech enhancement incorporate noisy phase in both amplitude estimation and signal reconstruction stages, in this paper we propose a probabilistic method to estimate the clean speech phase from noisy observation. Our proposed method consists of phase unwrapping followed by threshold-based temporal smoothing using von Mises phase priors. The proposed phase enhancement method leads to improved speech quality and intelligibility predicted by instrumental measures without explicit incorporation of amplitude enhancement.

Harmonic phase estimation in single-channel speech enhancement using von mises distribution and prior SNR

In single-channel speech enhancement the spectral amplitude of the noisy signal is often modified while the noisy spectral phase is directly employed for signal reconstruction. Recently, additional improvement in speech enhancement performance has been reported when the noisy phase is modified. In this work, we propose a Bayesian estimator for phase of harmonics given the noisy speech. The proposed estimator relies on the fundamental frequency and the signal-to-noise ratio at harmonics. Throughout our experiments, we evaluate the performance of the proposed phase enhancement in comparison with the noisy phase, a benchmark and the clean phase as the upper-bound. The proposed method leads to joint improvement in quality and intelligibility at different SNRs and noise types.

Using DecaWave UWB transceivers for high-accuracy multipath-assisted indoor positioning

Robust indoor positioning and location awareness at a sub-meter accuracy typically require highly accurate radio channel measurements to extract precise time-of-flight measurements. Emerging UWB transponders like the DecaWave DW1000 chip offer to estimate channel impulse responses with reasonably high bandwidth and excellent clock stability, yielding a ranging precision below 10 cm. The competitive pricing of these chips allows scientists and engineers for the first time to exploit the benefits of UWB for indoor positioning without the need for a massive investment into experimental equipment. This work investigates the performance of the DW1000 chip concerning position related information that can be extracted from its channel impulse response measurements. We evaluate the signal-to-interference-plus-noise ratio of the line-of-sight and reflected multipath components which is a key parameter determining the Cramér-Rao lower bound on the ranging error variance. We propose a novel and highly efficient positioning algorithm, which requires information from a single anchor only. Results demonstrate reliable and robust positioning at an accuracy below 0.5 m.

Iterative joint MAP single-channel speech enhancement given non-uniform phase prior

Within the last three decades research in single-channel speech enhancement has been mainly focused on filtering the noisy spectral amplitude without that much focus on the integration of phase-based signal processing. Recently, several phase-aware algorithms based on phase-sensitive signal models were proposed for speech enhancement using the minimum mean squared error (MMSE). Improved performance over the conventional phase-insensitive approaches has been achieved. In this paper, we propose an iterative joint maximum a posteriori (MAP) amplitude and phase estimator (ijMAP) assuming a non-uniform phase distribution. Experimental results demonstrate the effectiveness of the proposed method in recovering both amplitude and phase in noise, justified by perceived quality, speech intelligibility and phase estimation error instrumental measures. The proposed method, brings joint improvement in perceived quality and speech intelligibility compared to the phase-blind joint MAP estimator with a comparable performance to the complex MMSE estimator.

Bandwidth dependence of the ranging error variance in dense multipath

It is well known that the time-of-flight ranging performance is heavy influenced by multipath propagation within a radio environment. This holds in particular in dense multipath channels as encountered in indoor scenarios. The signal bandwidth has a tremendous influence on this effect, as it determines whether the time resolution is sufficient to resolve the useful line-of-sight (LOS) signal component from interfering multipath. This paper employs a geometry-based stochastic channel model to analyze and characterize the ranging error variance as a function of the bandwidth, covering the narrowband up to the UWB regimes. The Cramér-Rao lower bound (CRLB) is derived for this purpose. It quantifies the impact of bandwidth, SNR, and parameters of the multipath radio channel and can thus be used as an effective and accurate channel model (e.g.) for the cross-layer optimization of positioning systems. Experimental data are analyzed to validate our theoretical results.

Cooperative Multipath-Assisted Navigation and Tracking: A Low-Complexity Approach

Wireless localization has become a key technology for cooperative agent networks. However, for many applications, it is still illusive to reach the desired level of accuracy and robustness, especially in indoor environments which are characterized by harsh multipath propagation. In this work we introduce a cooperative low-complexity algorithm that utilizes multipath components for localization instead of suffering from them. The algorithm uses two types of measurements: (i) bistatic measurements between agents and (ii) monostatic (bat-like) measurements by the individual agents. Simulations that use data generated from a realistic channel model, show the applicability of the methodology and the high level of accuracy that can be reached.

UHF-RFID backscatter channel analysis for accurate wideband ranging

Positioning and ranging within UHF RFID are highly dependent on the channel characteristics. The accuracy of time-of-flight based ranging systems is fundamentally limited by the available bandwidth. This paper first analyzes the UHF RFID backscatter channel formed by convolution of the individual constituent channels. For this purpose, we present comprehensive wideband channel measurements in two representative scenarios and an analysis with respect to the Rician K-factor for the line-of-sight component, the root-mean-square delay spread, and the coherence distance, which all influence the potential positioning performance. Based on these measurements, we validate the Cramér Rao lower bound for time-of-flight based ranging under the influence of dense multipath and present two types of range estimators, a maximum likelihood and a matched filter approach. The resulting range estimates highlight the need for an increased bandwidth for UHF RFID systems with respect to time-of-flight based ranging.