Unsupervised detection of acoustic events using information bottleneck principle

Li, Yanxiong; Wang, Qin; Li, Xianku; Xue, Zhang; Zhang, Yuhan; Chen, Aiwu; He, Qianhua; Huang, Qian

doi:10.1016/j.dsp.2016.12.012

Cited by 10 publications

(1 citation statement)

References 13 publications

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“…where N is the number of audio clips, and xi is the log-mel of the ith audio clip. The clustering loss Lc is defined as a Kullback-Leibler (KL) divergence [28], [31] between the distribution of soft labels Q = {qi} and the predefined target distribution P={pi}. Lc is computed by…”

Section: Joint Lossmentioning

confidence: 99%

Domestic Activities Clustering From Audio Recordings Using Convolutional Capsule Autoencoder Network

Lin

Huang

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Recent efforts have been made on domestic activities classification from audio recordings, especially the works submitted to the challenge of DCASE (Detection and Classification of Acoustic Scenes and Events) since 2018. In contrast, few studies were done on domestic activities clustering, which is a newly emerging problem. Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster. Domestic activities clustering is an effective way for unsupervised estimation of daily activities performed in home environment. In this study, we propose a method for domestic activities clustering using a convolutional capsule autoencoder network (CCAN). In the method, the deep embeddings are learned by the autoencoder in the CCAN, while the deep embeddings which belong to the same class of domestic activities are merged into a single cluster by a clustering layer in the CCAN. Evaluated on a public dataset adopted in DCASE-2018 Task 5, the results show that the proposed method outperforms state-of-the-art methods in terms of the metrics of clustering accuracy and normalized mutual information.

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Section: Joint Lossmentioning

confidence: 99%