Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures

Seetharaman, Prem; Wichern, Gordon; Roux, Jonathan Le; Pardo, Bryan

doi:10.1109/icassp.2019.8683198

Cited by 28 publications

(22 citation statements)

References 29 publications

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“…Ensemble methods have seen rapid growth in the past decade within the machine learning community [39]. An ensemble is a group of predictors, each of which gives an estimate of a response variable.…”

Section: Background Materialsmentioning

confidence: 99%

A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project

Barmpas¹,

Tasoulis²,

Ag³

et al. 2021

Preprint

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The ATHLOS cohort is composed of several harmonized datasets of international cohorts related to health and aging. The healthy aging scale has been constructed based on a selection of particular variables from 16 individual studies. In this paper, we consider a selection of additional variables found in ATHLOS and investigate their utilization for predicting the healthy aging. For this purpose motivated by the dataset’s volume and diversity we focus our attention upon the clustering for prediction scheme, where unsupervised learning is utilized to enhance prediction power, showing the predictive utility of exploiting structure in the data by clustering. We show that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering within a clustering for ensemble classification scheme while retaining prediction benefits. We propose a complete methodology which is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A strait-forward open source implementation is provided for the R project.

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Section: Background Materialsmentioning

confidence: 99%

A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project

Barmpas¹,

Tasoulis²,

Ag³

et al. 2021

Preprint

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show abstract

“…Many techniques reported consisted of multiple stages separately optimized under different criteria, such as signal representation and embedding [15]. Some embedding clustering methods add phase information on multi-channel, and other research concerns the low-delay of deep clustering approaches [16], [17]. Orthogonal deep clustering improves the separation performance of the model by adding an orthogonal constraint penalty term of the objective function to reduce the correlation between the embedded expression [18].…”

Section: Related Work a Speech Separation Based On Deep Clusteringmentioning

confidence: 99%

Graph Convolution-Based Deep Clustering for Speech Separation

Qin

Jiang

et al. 2020

IEEE Access

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Deep clustering is a promising technique for speech separation that is crucial to speech communication, acoustic target detection, acoustic enhancement and speech recognition. In the study of monophonic speech separation, the problem is that the decrease in separation and generalization performance of the model in the case of reducing the variety of the training data set. In this paper, we propose a comprehensive deep clustering framework that construction the structural speech data based on GCN, named graph deep clustering (GDC) to further improve the separation performance of the separation model. In particular, embedding features are transformed into graph-structured data, and the speech separation mask is achieved by clustering these graph-structured data. Graph structural information aggregates nodes within a class, which makes feature representations conducive to clustering. Experimental results demonstrate that the proposed scheme can improve the clustering performance. The SDR of the separated speech is improved by about 1.2 dB, and the clustering accuracy is improved by 15%. We also use the perceptually motivated objective measures for the evaluation of audio source separation to score the speech quality. The target speech quality and the overall perceptual score are improved by 10.7% compared with other speech separation algorithms. INDEX TERMS Construction of graph-structured data, deep clustering, graph convolutional filter, speech separation.

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“…They reported that the cACGMM performance was improved by initializing it with the pre-trained separation network. Seetharaman et al [13] designed a loss function weighted by a confidence measure of the estimated references. Drude et al [15] also proposed a novel approach that directly trains a separation network from the cACGMM likelihood.…”

Section: Unsupervised Training Of Neural Source Separationmentioning

confidence: 99%

“…Unsupervised training for neural source separation using multichannel mixture signals has recently gained a lot of attention [12][13][14][15]. One approach is to generate supervised data by using multichannel separation methods [12][13][14]. This approach suffers from the estimation errors of the multichannel methods mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Deep Bayesian Unsupervised Source Separation Based On A Complex Gaussian Mixture Model

Bando

Sasaki

Yoshii

2019

2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)

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This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of supervised data to achieve excellent performance. Although multichannel methods based on spatial information can work without such training data, they are often sensitive to parameter initialization and degraded with the sources located close to each other. The proposed method uses a cost function based on a spatial model called a complex Gaussian mixture model (cGMM). This model has the time-frequency (TF) masks and direction of arrivals (DoAs) of sources as latent variables and is used for training separation and localization networks that respectively estimate these variables. This joint training solves the frequency permutation ambiguity of the spatial model in a unified deep Bayesian framework. In addition, the pre-trained network can be used not only for conducting monaural separation but also for efficiently initializing a multichannel separation algorithm. Experimental results with simulated speech mixtures showed that our method outperformed a conventional initialization method.

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Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures

Cited by 28 publications

References 29 publications

A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project

A Hybrid Machine Learning Framework for Enhancing the Prediction Power in Large Scale Population Studies: The ATHLOS Project

Graph Convolution-Based Deep Clustering for Speech Separation

Deep Bayesian Unsupervised Source Separation Based On A Complex Gaussian Mixture Model

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