Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Zhu, Qiaoxi"'
Autor:
Zhang, Hejing, Zhu, Qiaoxi, Guan, Jian, Liu, Haohe, Xiao, Feiyang, Tian, Jiantong, Mei, Xinhao, Liu, Xubo, Wang, Wenwu
First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availabilit
Externí odkaz:
http://arxiv.org/abs/2310.14173
Autor:
Guan, Jian, Liu, Youde, Kong, Qiuqiang, Xiao, Feiyang, Zhu, Qiaoxi, Tian, Jiantong, Wang, Wenwu
Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-base
Externí odkaz:
http://arxiv.org/abs/2310.08950
Data-driven approaches hold promise for audio captioning. However, the development of audio captioning methods can be biased due to the limited availability and quality of text-audio data. This paper proposes a SynthAC framework, which leverages rece
Externí odkaz:
http://arxiv.org/abs/2309.09705
Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift, where the type of domain shift is considered in feature learning by incorporating section IDs. However, the attributes accomp
Externí odkaz:
http://arxiv.org/abs/2309.07498
Different machines can exhibit diverse frequency patterns in their emitted sound. This feature has been recently explored in anomaly sound detection and reached state-of-the-art performance. However, existing methods rely on the manual or empirical d
Externí odkaz:
http://arxiv.org/abs/2308.14063
Time-weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection
Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer
Externí odkaz:
http://arxiv.org/abs/2305.03328
Existing contrastive learning methods for anomalous sound detection refine the audio representation of each audio sample by using the contrast between the samples' augmentations (e.g., with time or frequency masking). However, they might be biased by
Externí odkaz:
http://arxiv.org/abs/2304.03588
State-of-the-art audio captioning methods typically use the encoder-decoder structure with pretrained audio neural networks (PANNs) as encoders for feature extraction. However, the convolution operation used in PANNs is limited in capturing the long-
Externí odkaz:
http://arxiv.org/abs/2304.03586
Autor:
Kong, Qiuqiang, Liu, Shilei, Shi, Junjie, Ye, Xuzhou, Cao, Yin, Zhu, Qiaoxi, Xu, Yong, Wang, Yuxuan
Sound field decomposition predicts waveforms in arbitrary directions using signals from a limited number of microphones as inputs. Sound field decomposition is fundamental to downstream tasks, including source localization, source separation, and spa
Externí odkaz:
http://arxiv.org/abs/2210.12345
Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, maki
Externí odkaz:
http://arxiv.org/abs/2201.05510