Mobile Phone Clustering From Speech Recordings Using Deep Representation and Spectral Clustering

Li, Yanxiong; Xue, Zhang; Li, Xianku; Zhang, Yuhan; Yang, Jian; He, Qianhua

doi:10.1109/tifs.2017.2774505

Cited by 25 publications

(16 citation statements)

References 38 publications

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“…Both clustering accuracy (CA) and normalized mutual information (NMI) are used as performance metrics. They have been popularly adopted as metrics for clustering, and their details are referred to [31]. The higher their values are, the better the performance is.…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…Unsupervised training extracted features by changing the data itself to extract feature data that could reflect the original. Inspired by this, Yanxiong Li et al [12,24] proposed two types of deep features: the first used MFCC features to build a deep neural network, and then extracted the output of the middle layer of the DNN network as the feature; the second used MFCC feature train a deep auto-encoding network and then used the output of the middle layer as the output feature. Experiments showed that the deep feature used by the author is better than the available feature.…”

Section: Devices Source Information Based On Deep Featuresmentioning

confidence: 99%

“…Deep learning techniques can automatically extract highly abstract and complex features from raw data due to their powerful representation learning ability. CNN (convolutional neural networks) [11] and DNN (Deep Neural Networks) [12] are effective methods for device source identification, extracting the spatial correlation of related feature domains. LSTM (Long Short-Term Memory) can reasonably handle the temporal correlation in sequential data [13], which has certain advantages in the sequence model with long-term memory.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the success of deep learning for feature representation [12] and RNN for music classification tasks [13], we propose a device source identification method based on the end-to-end framework for the fusion of spatial and temporal features. In this proposed method, first, to fully explore the spatial information and temporal information of device source, spatial and temporal feature extraction networks are built to extract spatial and temporal information from GSV feature and MFCC feature.…”

Section: Introductionmentioning

confidence: 99%

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Spatial and temporal learning representation for end-to-end recording device identification

Zeng

Zhu

Wang

et al. 2021

EURASIP J. Adv. Signal Process.

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Deep learning techniques have achieved specific results in recording device source identification. The recording device source features include spatial information and certain temporal information. However, most recording device source identification methods based on deep learning only use spatial representation learning from recording device source features, which cannot make full use of recording device source information. Therefore, in this paper, to fully explore the spatial information and temporal information of recording device source, we propose a new method for recording device source identification based on the fusion of spatial feature information and temporal feature information by using an end-to-end framework. From a feature perspective, we designed two kinds of networks to extract recording device source spatial and temporal information. Afterward, we use the attention mechanism to adaptively assign the weight of spatial information and temporal information to obtain fusion features. From a model perspective, our model uses an end-to-end framework to learn the deep representation from spatial feature and temporal feature and train using deep and shallow loss to joint optimize our network. This method is compared with our previous work and baseline system. The results show that the proposed method is better than our previous work and baseline system under general conditions.

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“…have the ability to group together data with high similarities, which generates cluster labels [ 36 ]. This technique has produced reasonable results in areas such as trajectory annotation [ 37 , 38 ] and speech recording [ 39 ] among others. However, validating results from clustering algorithms still remains a major challenge [ 40 , 41 ].…”

Section: Related Workmentioning

confidence: 99%

Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors

Owoh

Singh

Zaaba

2018

Sensors

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Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.

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Mobile Phone Clustering From Speech Recordings Using Deep Representation and Spectral Clustering

Cited by 25 publications

References 38 publications

Domestic Activities Clustering From Audio Recordings Using Convolutional Capsule Autoencoder Network

Domestic Activities Clustering From Audio Recordings Using Convolutional Capsule Autoencoder Network

Spatial and temporal learning representation for end-to-end recording device identification

Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors

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