Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering

Fakhry, Mahmoud; Poorjam, Amir Hossein; Christensen, Mads Græsbøll

doi:10.23919/eusipco.2018.8553123

Cited by 9 publications

(4 citation statements)

References 23 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unsupervised methods do not assume any prior knowledge about identity of the speaker or noise environment. The supervised methods, on the other hand, make use of training data to train [45], [46], non-negative matrix factorization based methods [10], [47] and the DNN-based methods [48]. In the supervised method, the speech and noise statistics/parameters estimated using the training data are exploited within a filter to remove the noise components from the noisy observation.…”

Section: B Noise Reductionmentioning

confidence: 99%

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

Poorjam

Kavalekalam

Shi

et al. 2021

Speech Communication

Self Cite

View full text Add to dashboard Cite

Section: B Noise Reductionmentioning

confidence: 99%

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

Poorjam

Kavalekalam

Shi

et al. 2021

Speech Communication

Self Cite

View full text Add to dashboard Cite

“…The goal is to identify as many low-quality and inconsistent samples as possible which are outliers with respect to the majority of samples in the database. Depending on the application, a flagged outlier can either be kept in the database if it has been a false alarm, be enhanced and kept in the database if it is degraded [19], or be excluded from the database if it is not possible to retrieve useful information from the signal.…”

Section: Background a Problem Formulationmentioning

confidence: 99%

Quality Control in Remote Speech Data Collection

Poorjam

Little

Jensen

et al. 2019

IEEE J. Sel. Top. Signal Process.

Self Cite

View full text Add to dashboard Cite

There is the need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers which deserve further investigation. In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold. The DetMCD is a computationally efficient algorithm which provides a highly robust estimate of the mean and covariance in multivariate data even when 50% of the data are outliers. Experimental results using 8 different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in significantly decreasing the effort required for manually controlling the quality of the database.

show abstract

“…A variety of effective signal enhancement techniques have been developed to enhance a degraded speech signal such as noise reduction [5,6], dereverberation [7,8], and restoration of some types of nonlinear distortion [9,10]. Most of these enhancement algorithms have been designed to deal with a specific type of degradation in a signal, although recent research in comprehensive speech enhancement, dealing with both additive noise and reverberation, is promising [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A CNN-based approach to identification of degradations in speech signals

Saishu

Poorjam

Christensen

2021

J AUDIO SPEECH MUSIC PROC.

Self Cite

View full text Add to dashboard Cite

The presence of degradations in speech signals, which causes acoustic mismatch between training and operating conditions, deteriorates the performance of many speech-based systems. A variety of enhancement techniques have been developed to compensate the acoustic mismatch in speech-based applications. To apply these signal enhancement techniques, however, it is necessary to know prior information about the presence and the type of degradations in speech signals. In this paper, we propose a new convolutional neural network (CNN)-based approach to automatically identify the major types of degradations commonly encountered in speech-based applications, namely additive noise, nonlinear distortion, and reverberation. In this approach, a set of parallel CNNs, each detecting a certain degradation type, is applied to the log-mel spectrogram of audio signals. Experimental results using two different speech types, namely pathological voice and normal running speech, show the effectiveness of the proposed method in detecting the presence and the type of degradations in speech signals which outperforms the state-of-the-art method. Using the score weighted class activation mapping, we provide a visual analysis of how the network makes decision for identifying different types of degradation in speech signals by highlighting the regions of the log-mel spectrogram which are more influential to the target degradation.

show abstract

Speech Enhancement by Classification of Noisy Signals Decomposed Using NMF and Wiener Filtering

Cited by 9 publications

References 23 publications

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

Quality Control in Remote Speech Data Collection

A CNN-based approach to identification of degradations in speech signals

Contact Info

Product

Resources

About