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Automatic segmentation of infant cry signals using hidden Markov models.

, , , , , and . EURASIP J. Audio Speech Music. Process., (2018)

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Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters., , , , and . CoRR, (2019)Memory Requirement Reduction of Deep Neural Networks for Field Programmable Gate Arrays Using Low-Bit Quantization of Parameters., , , , and . EUSIPCO, page 466-470. IEEE, (2020)Low latency sound source separation using convolutional recurrent neural networks., , , , , and . WASPAA, page 71-75. IEEE, (2017)Subjective Evaluation of Deep Neural Network Based Speech Enhancement Systems in Real-World Conditions., , , , , and . MMSP, page 1-6. IEEE, (2022)Deep Neural Network Based Low-Latency Speech Separation with Asymmetric Analysis-Synthesis Window Pair., , , and . EUSIPCO, page 301-305. IEEE, (2021)Low-latency sound source separation using deep neural networks., , , , and . GlobalSIP, page 272-276. IEEE, (2016)Automatic segmentation of infant cry signals using hidden Markov models., , , , , and . EURASIP J. Audio Speech Music. Process., (2018)Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications., , , and . CoRR, (2018)Dynamic Processing Neural Network Architecture For Hearing Loss Compensation., , , , and . CoRR, (2023)Dynamic Processing Neural Network Architecture for Hearing Loss Compensation., , , , and . IEEE ACM Trans. Audio Speech Lang. Process., (2024)