MetaRL-SE: a few-shot speech enhancement method based on meta-reinforcement learning

Zhou, Weili; Ji, Ruijie; Lai, Jinxiong

doi:10.1007/s11042-023-14945-6

Cited by 1 publication

(2 citation statements)

References 24 publications

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“…Zhou, W., et al [17], suggested a Meta-reinforcement learning paradigm by concentrating on few-shot learning for improving speech. The experiment's findings demonstrate that in comparison to state-of-the-art DNN-based SE methods under difficult conditions, where the environment noises are varied and the signals are non-stationary, this work achieves at least improvements of 1.3%~12.5% for a single shot and 3.1%~14.3% for a five-shot scenario.…”

Section: Literature Surveymentioning

confidence: 99%

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Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

Pathrose,

Ismail M,

Mohan P

2023

Int. j. electr. comput. eng. syst. (Online)

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In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments.

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Section: Literature Surveymentioning

confidence: 99%

“…Eq. ( 12) is evaluated to determine a clan center's value: (17) Where the number of elephants in the clan is signified as N m x , the k th dimension of an individual elephant. In Eq.…”

Section: Fig 3 Flow Diagram Of the Proposed Enhance Ehomentioning

confidence: 99%