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To demonstrate the benefit of sparseness control, the proposed SC-RPAPA algorithm was simulated using an echo path change from the sparse to the dispersive impulse response in Fig. 3.
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In both cases, functional MRI images were acquired using an echo planar T2*-weighted gradient echo sequence (echo time = 51 ms, repetition time = 3000 ms, flip angle= 83°).
Diffusion-weighted images were acquired using an echo-planar, spin-echo, sequence.
Don't use an echo pedal.
Using this estimated echo path, an estimate of the acoustic echo signal is generated which is then subtracted from the microphone signal.
As double-talk detection is out of the scope of this article, we will assume this to be handled separately, such that the microphone signal during time intervals used for echo path estimation only consists of far-end signal components (acoustic echoes).
The most simple, yet frequently used echo path model is the linear model, depicted in Fig. 2 a. Fig. 2 a d Frequently used echo path models for AEC, where each of the red blocks represents a fixed number of coefficients of a linear Finite Impulse Response (FIR) filter.
Functional images were collected over six runs using a gradient-echo echo-planar sequence (TR=2 s; echo times=28.0 ms; flip angle=90°; 40 axial slices; 3 × 3 × 3 mm).
The remainder of this paper is structured as follows: after introducing the notation in Section 2, frequently used echo path models and their adaptation are reviewed in Sections 3.1 and 3.2, respectively.
BOLD signals were obtained using a spin-echo echo-planar imaging sequence at 7 T.
Images were acquired at 1.5 T using a gradient-echo echo-planar imaging sequence sensitive to blood oxygenation level-dependent (BOLD) contrast.
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