Titlebook: Man-Machine Speech Communication; 18th National Confer Jia Jia,Zhenhua Ling,Zixing Zhang Conference proceedings 2024 The Editor(s) (if appl

Friction

Ultra-Low Complexity Residue Echo and Noise Suppression Based on Recurrent Neural Network,ifferent topologies, feed in with different combinations of the far-end signal, microphone signal, predicted linear echo and residue error signal. The proposed RES models can achieve comparable echo cancellation and noise reduction capabilities to the AEC Challenge 2022 baseline model at a complexity lower than 5% of the baseline model.

落葉劑

artless

Ergots

,Data Augmentation by?Finite Element Analysis for?Enhanced Machine Anomalous Sound Detection,and the material of the medium is modified to acquire data from multiple domains. The experimental results on DCASE?2023 Task?2 dataset indicates a better performance from models trained using augmented data.

Fluctuate

,End-to-End Streaming Customizable Keyword Spotting Based on?Text-Adaptive Neural Search,esults on various datasets show that our approach significantly outperforms both traditional post-processing baseline and the neural search baseline, meanwhile achieving a 44x search speedup compared to the traditional post-processing method.

champaign

,Real-Time Automotive Engine Sound Simulation with?Deep Neural Network,modified griffin-lim algorithm at the frame level, which, with our proposed overlap-and-add feature, can bridge the phase gap between two frames. Experimental evaluations on our self-collected database validate the efficacy of the introduced approach.

Onerous

,A Lightweight Music Source Separation Model with?Graph Convolution Network,and multiple .s, and also make a visualization analysis of the main components in the G-MSS network. Comparing with the other 13 methods on the MUSDB18 dataset, our proposed G-MSS achieves comparable separation performance while maintaining the lower amount of parameters.

SSRIS

CAMP

,A Study on?Domain Adaptation for?Audio-Visual Speech Enhancement,-model fusion strategy to enhance the overall model performance further. Our system exhibited significant improvements in all objective metrics, including PESQ, STOI, and SiSDR, compared to almost all competing teams. As a result, our system ranked the 2nd place in the objective metrics comparison for track 1.

BARB