In this paper, we consider the problem of estimating the target sound direction of arrival (DoA) for a hearing aid (HA) system, which can connect to a wireless microphone worn by the talker of interest. The wireless microphone “informs” the HA system about the noise-free target speech. To estimate the DoA, we consider a maximum-likelihood approach, and we assume that a database of DoA-dependent relative transfer functions (RTFs) has been measured in advance and is available. The proposed DoA estimator is able to take the available noise-free target speech, ambient noise characteristics, and the shadowing effect of the user's head on the received signals into account, and it supports both monaural and binaural microphone array configurations. Moreover, we analytically analyze the bias in the proposed estimator and introduce a modified estimator, which has been compensated for the bias. We demonstrate that the proposed method has lower computational complexity and better performance than recent RTF-based estimators. Furthermore, to decrease the number of parameters required to be wirelessly exchanged between the HAs in binaural configurations, we propose an information fusion strategy, which avoids transmitting microphone signals between the HAs. An important benefit of the proposed IF strategy is that the number of parameters to be exchanged between the HAs is independent of the number of HA microphones. Finally, we investigate the performance of variants of the proposed estimator extensively in different noisy and reverberant situations.

Recent hearing aid systems (HASs) can connect to a wireless microphone worn by the talker of interest. This feature gives the HASs access to a noise-free version of the target signal. In this paper, we address the problem of estimating the target sound direction of arrival (DoA) for a binaural HAS given access to the noise-free content of the target signal. To estimate the DoA, we present a maximum-likelihood framework which takes the shadowing effect of the user's head on the received signals into account by modeling the relative transfer functions (RTFs) between the HAS's microphones. We propose three different RTF models which have different degrees of accuracy and individualization. Furthermore, we show that the proposed DoA estimators can be formulated in terms of inverse discrete Fourier transforms to evaluate the likelihood function computationally efficiently. We extensively assess the performance of the proposed DoA estimators for various DoAs, signal to noise ratios, and in different noisy and reverberant situations. The results show that the proposed estimators improve the performance markedly over other recently proposed “informed” DoA estimator.

In this paper, we propose a Direction of Arrival (DoA) estimator for a Hearing Aid System (HAS) which can connect to a wireless microphone worn by a target talker. The wireless microphone "informs" the HAS about the almost noise-free content of the target sound, and the proposed DoA estimator uses the knowledge of the noise-free target sound and the received microphone signals to estimate the DoA via a maximum likelihood approach. Moreover, the proposed DoA estimator resorts to a user-independent spherical-head model to consider the acoustic impacts of the head on the received signals at the HAS. Further, the proposed DoA estimator uses an Inverse Discrete Fourier Transform (IDFT) technique to evaluate the likelihood function computationally efficiently. We assessed the performance of the proposed estimator for various DoAs, Signal to Noise Ratios (SNRs), and target distances in different noisy and reverberant situations. The proposed estimator improves the performance markedly over other recently proposed "informed" DoA estimators.

Most state-of-the-art Sound Source Localization (SSL) algorithms have been proposed for applications which are “uninformed” about the target sound content; however, utilizing a wireless microphone worn by a target talker, enables recent Hearing Aid Systems (HASs) to access to an almost noise-free sound signal of the target talker at the HAS via the wireless connection. Therefore, in this paper, we propose a maximum likelihood (ML) approach, which we call MLSSL, to estimate the Direction of Arrival (DoA) of the target signal given access to the target signal content. Compared with other “informed” SSL algorithms which use binaural microphones for localization, MLSSL performs better using signals of one or more microphones placed on just one ear, thereby reducing the wireless transmission overhead of binaural hearing aids. More specifically, when the target location confined to the front-horizontal plane, MLSSL shows an average absolute DoA estimation error of 5 degrees at SNR of -5 dB in a large-crowd noise and non-reverberant situation. Moreover, MLSSL suffers less from front-back confusions compared with the recent approaches.

The direction dependence of Head Related Transfer Functions (HRTFs) forms the basis for HRTF-based Sound Source Localization (SSL) algorithms. In this paper, we show how spectral similarities of the HRTFs of different directions in the horizontal plane influence performance of HRTF-based SSL algorithms; the more similar the HRTFs of different angles to the HRTF of the target angle, the worse the performance. However, we also show how the microphone array geometry can assist in differentiating between the HRTFs of the different angles, thereby improving performance of HRTF-based SSL algorithms. Furthermore, to demonstrate the analysis results, we show the impact of HRTFs similarities and microphone array geometry on an exemplary HRTF-based SSL algorithm, called MLSSL. This algorithm is well-suited for this purpose as it allows to estimate the Direction-of-Arrival (DoA) of the target sound using any number of microphones and any geometries of the microphone array around the head.

Motion estimation is one of the key tools to achieve a very low bit rate in video coding. The selection of optimum motion vectors (MV) has a significant impact on the quality of the resulted compressed video, in Rate-Distortion (R-D) sense. The established method uses the Lagrange multiplier to optimally select the MV for each block. However, it does not consider the effect of residual coding at the same time with the effect of MVs. In this paper, we have considered the effect of residual coding in motion vector (MV) selection with modeling motion estimation and residual coding as independent processes which results in a new optimization condition. It is based on local optimization, which is the bit allocation between motion estimation and residual coding, and global optimization, which is the bit allocation among different blocks. The proposed motion estimation algorithm results in a PSNR improvement of 0.5 - 3.0 dB when it is used in the H.264 standard with block size of 4 times 4.