Real-time Speech Extraction Using Spatially Regularized Independent Low-rank Matrix Analysis and Rank-constrained Spatial Covariance Matrix Estimation
<|reference_start|>Real-time Speech Extraction Using Spatially Regularized Independent Low-rank Matrix Analysis and Rank-constrained Spatial Covariance Matrix Estimation: Real-time speech extraction is an important challenge with various applications such as speech recognition in a human-like avatar/robot. In this paper, we propose the real-time extension of a speech extraction method based on independent low-rank matrix analysis (ILRMA) and rank-constrained spatial covariance matrix estimation (RCSCME). The RCSCME-based method is a multichannel blind speech extraction method that demonstrates superior speech extraction performance in diffuse noise environments. To improve the performance, we introduce spatial regularization into the ILRMA part of the RCSCME-based speech extraction and design two regularizers. Speech extraction experiments demonstrated that the proposed methods can function in real time and the designed regularizers improve the speech extraction performance.<|reference_end|>
@article{ishikawa2024real-time,
    title={Real-time Speech Extraction Using Spatially Regularized Independent
  Low-rank Matrix Analysis and Rank-constrained Spatial Covariance Matrix
  Estimation},
    author={Yuto Ishikawa, Kohei Konaka, Tomohiko Nakamura, Norihiro Takamune,
  Hiroshi Saruwatari},
    journal={arXiv preprint arXiv:2403.12477},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12477},
    primaryClass={cs.SD eess.AS}
}