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PDPCRN: Parallel Dual-Path CRN with Bi-directional Inter-Branch Interactions for Multi-Channel Speech Enhancement (2309.10379v1)
Published 19 Sep 2023 in cs.SD and eess.AS
Abstract: Multi-channel speech enhancement seeks to utilize spatial information to distinguish target speech from interfering signals. While deep learning approaches like the dual-path convolutional recurrent network (DPCRN) have made strides, challenges persist in effectively modeling inter-channel correlations and amalgamating multi-level information. In response, we introduce the Parallel Dual-Path Convolutional Recurrent Network (PDPCRN). This acoustic modeling architecture has two key innovations. First, a parallel design with separate branches extracts complementary features. Second, bi-directional modules enable cross-branch communication. Together, these facilitate diverse representation fusion and enhanced modeling. Experimental validation on TIMIT datasets underscores the prowess of PDPCRN. Notably, against baseline models like the standard DPCRN, PDPCRN not only outperforms in PESQ and STOI metrics but also boasts a leaner computational footprint with reduced parameters.
- “The benefit of binaural hearing in a cocktail party: Effect of location and type of interferer,” The Journal of the Acoustical Society of America, vol. 115, no. 2, pp. 833–843, 2004.
- Microphone array signal processing, vol. 1, Springer Science & Business Media, 2008.
- “A consolidated perspective on multimicrophone speech enhancement and source separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 25, no. 4, pp. 692–730, 2017.
- “Improved delay-and-sum beamforming algorithm for breast cancer detection,” International Journal of Antennas and Propagation, vol. 2008, 2008.
- “On optimal frequency-domain multichannel linear filtering for noise reduction,” IEEE Transactions on audio, speech, and language processing, vol. 18, no. 2, pp. 260–276, 2009.
- “Superdirective microphone arrays,” in Microphone arrays, pp. 19–38. Springer, 2001.
- “Two heads are better than one: A two-stage complex spectral mapping approach for monaural speech enhancement,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1829–1843, 2021.
- “Multiple-target deep learning for lstm-rnn based speech enhancement,” in 2017 Hands-free Speech Communications and Microphone Arrays (HSCMA). IEEE, 2017, pp. 136–140.
- “T-gsa: Transformer with gaussian-weighted self-attention for speech enhancement,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 6649–6653.
- “A regression approach to speech enhancement based on deep neural networks,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 1, pp. 7–19, 2014.
- “Supervised speech separation based on deep learning: An overview,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 10, pp. 1702–1726, 2018.
- “Segan: Speech enhancement generative adversarial network,” arXiv preprint arXiv:1703.09452, 2017.
- “End-to-end waveform utterance enhancement for direct evaluation metrics optimization by fully convolutional neural networks,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 9, pp. 1570–1584, 2018.
- “Convolutional-recurrent neural networks for speech enhancement,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018, pp. 2401–2405.
- Ke Tan and De Liang Wang, “Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020.
- “Pruning convolutional neural networks for resource efficient inference,” arXiv preprint arXiv:1611.06440, 2016.
- “Dpcrn: Dual-path convolution recurrent network for single channel speech enhancement,” arXiv preprint arXiv:2107.05429, 2021.
- “Self-attention with relative position representations,” arXiv preprint arXiv:1803.02155, 2018.
- “Demystifying local vision transformer: Sparse connectivity, weight sharing, and dynamic weight,” arXiv preprint arXiv:2106.04263, vol. 2, no. 3, 2021.
- “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning. pmlr, 2015, pp. 448–456.
- “Gaussian error linear units (gelus),” arXiv preprint arXiv:1606.08415, 2016.
- “Image method for efficiently simulating small-room acoustics,” The Journal of the Acoustical Society of America, vol. 65, no. 4, pp. 943–950, 1979.
- “Speech database development at mit: Timit and beyond,” Speech communication, vol. 9, no. 4, pp. 351–356, 1990.
- “Assessment for automatic speech recognition: Ii. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems,” Speech communication, vol. 12, no. 3, pp. 247–251, 1993.
- “The third ‘chime’speech separation and recognition challenge: Dataset, task and baselines,” in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, 2015, pp. 504–511.
- “Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,” in 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. No. 01CH37221). IEEE, 2001, vol. 2, pp. 749–752.
- “An algorithm for intelligibility prediction of time–frequency weighted noisy speech,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 7, pp. 2125–2136, 2011.
- Ke Tan and DeLiang Wang, “Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 380–390, 2019.