Generalized Cross-correlation With Phase Transform Research Articles

High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

To improve the estimation accuracy, a novel time delay estimation (TDE) method based on the closed-form offset compensation is proposed. Firstly, we use the generalized cross-correlation with phase transform (GCC-PHAT) method to obtain the initial TDE. Secondly, a signal model using normalized cross spectrum is established, and the noise subspace is extracted by eigenvalue decomposition (EVD) of covariance matrix. Using the orthogonal relation between the steering vector and the noise subspace, the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE. Finally, the offsets are compensated via simple least squares (LS). Compared to other state-of-the-art methods, the proposed method significantly reduces the computational complexity and achieves better estimation performance. Experiments on both simulation and real-world data verify the efficiency of the proposed approach.

Direction of arrival estimation of partial sound sources of vehicles with a two-microphone array

The generalized cross-correlation with phase transform (GCC-PHAT) algorithm has proved to be useful for blindly estimating the direction of arrival of compact sound sources from microphone array recordings. In applications with distributions of partial sources, such as the tires of vehicles in urban environments, the GCC-PHAT needs to be improved, otherwise the detected sound directions change values between directions of the main sources or correspond to an intermediate value between these directions. This paper presents an extension of the GCC-PHAT, based on post-processing of the output delay matrix and on image processing techniques, in order to separately identify directions of the sound produced by the front and rear tires of moving vehicles. The proposed approach can be extended to identify the tire noise directions produced by vehicles with multiple axles. The algorithm performance is analyzed using pass-by measurements of two-axle vehicles, acquired by a two-microphone array. The experiments were conducted with passenger vehicles of four distinct models, running at different speeds. The experimental results show that the proposed method is able to estimate the vehicle speed with an average error of 10.8 km/h and the vehicle wheelbase with 26 cm on average. A possible application is multiple source characterization for parametric vehicle sound synthesis in auralization.

Double-Cross-Correlation Processing for Blind Sampling-Rate and Time-Offset Estimation

Coherent processing of signals captured by a wireless acoustic sensor network (WASN) requires an estimation of unknown parameters such as the sampling-rate and sampling-time offset (SRO and STO) of asynchronous sensor clocks. Although some sophisticated techniques for blind parameter estimation have become available in this young field of research, further development of new methods is required especially regarding environmental robustness, online estimation, and computational efficiency. As the main contribution of this work, we therefore introduce a novel approach for blind SRO estimation in the spirit of both a recently available time-domain double-cross-correlation processor (DXCP) and the well-known generalized cross-correlation with phase transform (GCC-PhaT). For the proposed approach, called DXCP-PhaT, we specifically introduce secondary cross-quantities to restore ergodicity of the otherwise drifting cross-correlation function or cross-spectrum of asynchronous input signals, which is an important property for estimation. Based upon this theoretical contribution of the paper, we then derive the DXCP-PhaT algorithm in the STFT domain, which brings along a number of improvements over state-of-the-art: advanced accuracy in terms of SRO assessment, environmental robustness to larger microphone distances as of real sensor networks, realtime-applicability in terms of online architecture and reduced complexity, and eventually an extension with online STO estimation (up to the inherent ambiguity of digital STO and acoustic TDOA) reliant on SRO compensation. Those claims are confirmed by comprehensive experiments, both offline and online, on simulated data and real recordings from a Rasberry-Pi-based WASN.

Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices

For certain applications, keyword spotting (KWS) requires some degree of personalization. This is the case for KWS for hearing assistive devices, e.g., hearing aids, where only the device user should be allowed to trigger the KWS system. In this paper, we first develop a new realistic hearing aid experimental framework. Next, using this framework we show that the performance of a state-of-the-art multi-task deep learning architecture exploiting cepstral features for joint KWS and users’ own-voice/external speaker detection drops significantly. To overcome this problem, we use phase difference information through GCC-PHAT (Generalized Cross-Correlation with PHAse Transform)-based coefficients along with log-spectral magnitude features. In addition, we demonstrate that working in the perceptually-motivated constant-Q transform (CQT) domain instead of in the short-time Fourier transform (STFT) domain allows for the generation of compact and coherent features which provide superior KWS performance. Our experimental results show that our CQT-based proposal achieves a relative KWS accuracy improvement of around 18% compared to using cepstral features while dramatically decreasing the number of multiplications in the multi-task architecture, which is key in the context of low-resource devices like hearing assistive devices.

A TDOA Estimation Method Through Phase Difference Square Sums (PDSS)

In order to improve the performance of TDOA estimation under multipath, non-line-of-sight and narrow signal bandwidth conditions, a time difference of arrival (TDOA) estimation method by Phase Difference Square Sum (PDSS) is introduced. Within the baseline time-lags of two separate receivers, PDSS between two received samples of the same signal are calculated for per lag; the TDOA is estimated by searching the minimum of PDSS. Under the condition of different signal-to-noise ratio (SNR) and Sample rates to Signal bandwidth ratio, the estimation error distribution of the PDSS are simulated, and are compared with the generalized cross correlation with phase transform (GCC-PHAT). In multipath propagation and non-line-of-sight environment of the metropolis, the estimation success rates are experimented to evaluate PDSS’s performance relative to GCC_PHAT.

Joint estimation of pitch and direction of arrival: improving robustness and accuracy for multi-speaker scenarios

In many speech communication applications, robust localization and tracking of multiple speakers in noisy and reverberant environments are of major importance. Several algorithms to tackle this problem have been proposed in the last decades. In this paper, we propose several extensions to a recently presented joint direction of arrival (DOA) and pitch estimation method, increasing its robustness in multi-speaker scenarios, noise, and reverberation. First, a spectral comb filter is added to the original algorithm to better cope with concurrent speakers. Second, the well-known generalized cross-correlation with phase transform (GCC-PHAT) is used as an additional weighting function to improve the DOA estimation accuracy in terms of correct hits. Third, using multiple microphone pairs, the multi-channel cross-correlation approach is incorporated to improve the robustness against noise and reverberation. In order to improve tracking for moving and even intersecting speakers, a particle filter is used. Experiments with real-world recordings in realistic acoustic conditions show that the proposed extensions increase the DOA hit rate by about 33% compared to the original algorithm for two step-wise moving sources at a signal-to-noise ratio (SNR) of 15 dB and a reverberation time RT60 of 560 ms.

AdaBoost based EMD as a De-Noising Technique in Time Delay Estimation Application

Estimation of time delay between signals received at two spatially separated sensors has considerable practical importance in the applications like source localization, direction finding etc., in RADAR, SONAR and other communication systems. In this paper cross correlation (CC) generalized cross correlation with phase transform (GCCPHAT) and maximum likelihood (ML) estimation methods are used as the time delay estimation methods. Prior to the delay estimation, the received signals are de-noised by AdaBoost based EMD technique. The performance of the delay estimation is significantly degraded by the signal-tonoise ratio (SNR) level and hence this factor has been considered as a principal factor. The simulation results of the proposed method are compared with the basic EMD as a denoising technique at various SNR levels. The results show that the proposed method improves the resolution in the delay estimation in the noisy environment.

Improved estimation of direction of arrival of sound sources for hearing aids using gyroscopic information

Determining the direction of arrival (DOA) of a sound source is important in spatial audio signal processing, as it can lead to substantial improvement in noise reduction performance. Techniques such as generalized cross correlation with phase transform (GCC-PHAT) and adaptive eigenvalue decomposition (AED) perform optimally when the measurement microphones are fixed in place. However, hearing-aid microphones move with the listener’s head movements, which can result in momentarily inaccurate directional estimates and noise artifacts in the output signal. Techniques such as GCC-PHAT experience degraded short-term performance in the presence of multiple signals and noise. The system presented measures instantaneous head movement velocity using a micro-electromechanical systems (MEMS) gyroscope attached to binaurally communicating hearing aids. Estimates of DOA for physically stationary sources are shifted based on the gyroscope's head-movement information. Using GCC-PHAT with gyroscopic input can produce robust in situ DOA estimates for several sources in reverberant environments. In addition, the gyroscope allows an adaptive beamformer to be steered to a target direction, compensating for head movements on a very short timescale during DOA estimation. Results show improved localization performance over a standard GCC-PHAT system during head movements.

Multi-source TDOA estimation in reverberant audio using angular spectra and clustering

We consider the problem of estimating the time differences of arrival (TDOAs) of multiple sources from a two-channel reverberant audio signal. While several clustering-based or angular spectrum-based methods have been proposed in the literature, only relatively small-scale experimental evaluations restricted to either category of methods have been carried out so far. We design and conduct the first large-scale experimental evaluation of these methods and investigate a two-step procedure combining angular spectra and clustering. In addition, we introduce and evaluate five new TDOA estimation methods inspired from signal-to-noise-ratio (SNR) weighting and probabilistic multi-source modeling techniques that have been successful for anechoic TDOA estimation and audio source separation. For 5cm microphone spacing, the best TDOA estimation performance is achieved by one of the proposed SNR-based angular spectrum methods. For larger spacing, a variant of the generalized cross-correlation with phase transform (GCC-PHAT) method performs best.