Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization
<|reference_start|>Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization: Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study proposes a new common component extraction method based on nonnegative tensor factorization (NTF) for higher model interpretability and more robust spotforming against hyperparameters. Moreover, attractor-based regularization was introduced to facilitate the automatic selection of optimal target bases in the NTF. Experimental results show that the proposed method performs better than conventional methods in spotforming performance and also shows some characteristics suitable for practical use.<|reference_end|>
@article{ayano2024audio,
    title={Audio Spotforming Using Nonnegative Tensor Factorization with
  Attractor-Based Regularization},
    author={Shoma Ayano, Li Li, Shogo Seki, Daichi Kitamura},
    journal={arXiv preprint arXiv:2407.08951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08951},
    primaryClass={cs.SD eess.AS}
}