Music removal by convolutional denoising autoencoder in speech recognition

Zhao, Mengyuan; Wang, Dong; Zhang, Zhiyong; Zhang, Xuewei

doi:10.1109/apsipa.2015.7415289

Cited by 27 publications

(15 citation statements)

References 16 publications

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“…An MLP based imputation approach was presented for MCAR missingness in [10] and also outperformed other statistical models. A Convolutional Denoising Autoencoder model which did not impute missing data but denoised audio signals was presented in [15]. A Denoising Autoencoder with more units in the encoder layer than input layer was presented in [5] and achieved good imputation results against MICE.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder

Safi

Beyer

Unnikrishnan

et al. 2020

Lecture Notes in Computer Science

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Missing data is a common occurrence in the time series domain, for instance due to faulty sensors, server downtime or patients not attending their scheduled appointments. One of the best methods to impute these missing values is Multiple Imputations by Chained Equations (MICE) which has the drawback that it can only model linear relationships among the variables in a multivariate time series. The advancement of deep learning and its ability to model non-linear relationships among variables make it a promising candidate for time series imputation. This work proposes a modified Convolutional Denoising Autoencoder (CDA) based approach to impute multivariate time series data in combination with a preprocessing step that encodes time series data into 2D images using Gramian Angular Summation Field (GASF). We compare our approach against a standard feed-forward Multi Layer Perceptron (MLP) and MICE. All our experiments were performed on 5 UEA MTSC multivariate time series datasets, where 20 to 50% of the data was simulated to be missing completely at random. The CDA model outperforms all the other models in 4 out of 5 datasets and is tied for the best algorithm in the remaining case.

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Section: Related Workmentioning

confidence: 99%

Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder

Safi

Beyer

Unnikrishnan

et al. 2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The decoder part reconstructs the input data from the low dimensional features. Convolutional denoising autoencoders (CDAEs) are similar to CAEs but CDAEs are trained from corrupted input signals and the encoder is used to extract noise robust features that the decoder can use to reconstruct a cleaned-up version of the input data [19,20]. The encoder part in CDAEs is composed of repetitions of a convolutional layer, an activation layer, and a pooling layer as shown in Fig.…”

Section: Fully Convolutional Denoising Autoencodersmentioning

confidence: 99%

“…CDAEs differ from conventional DAEs as their parameters (weights) are shared, which makes the CDAEs have fewer parameters than DAEs. The ability of CDAEs to extract repeating patterns in the input makes them suitable to be used to extract speech signals from background noise and music signals for speech enhancement and recognition [19,20].…”

Section: Introductionmentioning

confidence: 99%

Single channel audio source separation using convolutional denoising autoencoders

Grais

Plumbley

2017

2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDAEs) for monaural audio source separation. We use as many CDAEs as the number of sources to be separated from the mixed signal. Each CDAE is trained to separate one source and treats the other sources as background noise. The main idea is to allow each CDAE to learn suitable spectral-temporal filters and features to its corresponding source. Our experimental results show that CDAEs perform source separation slightly better than the deep feedforward neural networks (FNNs) even with fewer parameters than FNNs.Index Terms-Fully convolutional denoising autoencoders, single channel audio source separation, stacked convolutional autoencoders, deep convolutional neural networks, deep learning. Conv. ReLU Max pooling Conv. ReLU Max pooling Up sample Conv. ReLU Up sample Conv. ReLU EncoderDecoder

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“…Recently, CNN has also been applied to speech recognition [12,14] where again it achieved better recognition accuracy than DNN. Meanwhile, Zhao et al [15] proposed a music removal model based on CNN for speech recognition and obtained better recognition results compared with DNN. Hui et al also employed CNN to separate speech and noise by estimating the ideal ratio mask of the time-frequency units [16].…”

Section: Introductionmentioning

confidence: 99%

SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement

Fu¹,

Tsao²,

Lu³

2016

Interspeech 2016

146

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This paper proposes a signal-to-noise-ratio (SNR) aware convolutional neural network (CNN) model for speech enhancement (SE). Because the CNN model can deal with local temporal-spectral structures of speech signals, it can effectively disentangle the speech and noise signals given the noisy speech signals. In order to enhance the generalization capability and accuracy, we propose two SNR-aware algorithms for CNN modeling. The first algorithm employs a multi-task learning (MTL) framework, in which restoring clean speech and estimating SNR level are formulated as the main and the secondary tasks, respectively, given the noisy speech input. The second algorithm is an SNR adaptive denoising, in which the SNR level is explicitly predicted in the first step, and then an SNR-dependent CNN model is selected for denoising. Experiments were carried out to test the two SNR-aware algorithms for CNN modeling. Results demonstrate that CNN with the two proposed SNR-aware algorithms outperform the deep neural network counterpart in terms of standardized objective evaluations when using the same number of layers and nodes. Moreover, the SNR-aware algorithms can improve the denoising performance with unseen SNR levels, suggesting their promising generalization capability for real-world applications.

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Music removal by convolutional denoising autoencoder in speech recognition

Cited by 27 publications

References 16 publications

Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder

Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder

Single channel audio source separation using convolutional denoising autoencoders

SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement

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