• Journal of IKEEE
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• v.14 no.1
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• pp.1-7
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• 2010

This paper presents an implementation of the DisplayPort AUX(Auxiliary) Channel. DisplayPort uses Main link, AUX Channel and Hot Plug Detect line to transfer the video & audio data. For isochronous transport service, source device converts to image and audio data which are to be transported through the Main Link and transports the restructured image and audio data to sink device. The AUX Channel provides link service and device service for discovering, initializing and maintaining the Main link. Hot Plug Detect line is used to confirm the connection between source device and sink device. The AUX Channel is implemented with 3315 LUTs(Look Up Table), 1466 Flip Flops and 168.782MHz max speed synthesized using Xilinx ISE 9.2i at SoC Master3.

• Journal of IKEEE
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• v.14 no.4
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• pp.297-304
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• 2010

This paper presents a link layer design of DisplayPort interface with a state machine based on packet processing. The DisplayPort link layer provides isochronous video/audio transport service, link service, and device service. The merged video, audio main link, and AUX channel controller are implemented with 7,648 LUTs(Loop Up Tables), 6020 register, and 821,760 of block memory bits synthesized using a FPGA board and it operates at 203.32MHz.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.