Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 2022

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: CNN-LSTM

Sortuj według:

Ogranicz wyniki do:

Voice disorder classification using speech enhancement and deep learning models

Chaiani Mounira, Selouani Sid Ahmed, Boudraa Malika, Sidi Yakoub Mohammed

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 2

463--480

With the recent development of speech-enabled interactive systems using artificial agents, there has been substantial interest in the analysis and classification of voice disorders to provide more inclusive systems for people living with specific speech and language impairments. In this paper, a two-stage framework is proposed to perform an accurate classification of diverse voice pathologies. The first stage consists of speech enhancement processing based on the original premise, which considers impaired voice as a noisy signal. To put this hypothesis into practice, the noise lestral harmonic-tonoise ratio (CHNR). The second stage consists of a convolutional neural network with long short-term memory (CNN-LSTM) architecture designed to learn complex features from spectrograms of the first-stage enhanced signals. A new sinusoidal rectified unit (SinRU) is proposed to be used as an activation function by the CNN-LSTM network. The experiments are carried out by using two subsets of the Saarbruecken voice database (SVD) with different etiologies covering eight pathologies. The first subset contains voice recordings of patients with vocal cordectomy, psychogenic dysphonia, pachydermia laryngis and frontolateral partial laryngectomy, and the second subset contains voice recordings of patients with vocal fold polyp, chronic laryngitis, functional dysphonia, and vocal cord paresis. Dysarthria severity levels identification in Nemours and Torgo databases is also carried out. The experimental results showed that using the minimum mean square error (MMSE)-based signal enhancer prior to the CNN-LSTM network using SinRU, led to a significant improvement in the automatic classification of the investigated voice disorders and dysarhtria severity levels. These findings support the hypothesis that using an appropriate speech enhancement preprocessing has positive effects on the accuracy of the automatic classification of voice pathologies thanks to the reduction of the intrinsic noise induced by the voice impairment.

Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features

Muthukumaran Venkatesan, Natarajan Rajesh, Kaladevi Amarakundhi Chandrasekaran, Magesh Gopu, Babu Swapna

Acta Geophysica

2022

Vol. 70, no 6

2979--2990

Modern civilization has reported a significant rise in the volume of traffic on inland rivers all over the globe. Traffic flow prediction is essential for a good travel experience, but adequate computer processes for processing unpredictable spatiotemporal data (timestamp, weather, vessel_ID, water level, vessel_position, vessel_speed) in the inland water transportation industry are lacking. Moreover, such type of prediction relies primarily on past traffic patterns and perhaps other pertinent facts. Thus, we propose a deep learning-based computing process, namely Convolution Neural Network-Long Short-Term Memory Network (CNN-LSTM), a progressive predictor of employing uncertain spatiotemporal information to decrease navigation mishaps, traffic and flow prediction failures during transportation. Spatiotemporal correlation of current traffic flow may be processed using a simplified CNN-LSTM model. This hybridized prediction technique decreases update costs and meets the prediction needs with minimal computing overhead. A short case study on the waterways of the Indian state of Assam from Sandiya (27.835090 latitude, 95.658590 longitude) to Dhubri (26.022699 latitude, 89.978401 longitude) is undertaken to assess the model's performance. The evaluation of the suggested method includes a variety of trajectories of water transportation vehicles, including ferries, sailing boats, container ships, etc. The suggested approach outperforms conventional traffic flow predicting methods when it comes to short-term prediction with minimal predictive error (<2.75) and exhibited a major difference of more than 45% on the comparison of other methods.