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- Title
Ensemble System of Deep Neural Networks for Single-Channel Audio Separation.
- Authors
Al-Kaltakchi, Musab T. S.; Mohammad, Ahmad Saeed; Woo, Wai Lok
- Abstract
Speech separation is a well-known problem, especially when there is only one sound mixture available. Estimating the Ideal Binary Mask (IBM) is one solution to this problem. Recent research has focused on the supervised classification approach. The challenge of extracting features from the sources is critical for this method. Speech separation has been accomplished by using a variety of feature extraction models. The majority of them, however, are concentrated on a single feature. The complementary nature of various features have not been thoroughly investigated. In this paper, we propose a deep neural network (DNN) ensemble architecture to completely explore the complimentary nature of the diverse features obtained from raw acoustic features. We examined the penultimate discriminative representations instead of employing the features acquired from the output layer. The learned representations were also fused to produce a new features vector, which was then classified by using the Extreme Learning Machine (ELM). In addition, a genetic algorithm (GA) was created to optimize the parameters globally. The results of the experiments showed that our proposed system completely considered various features and produced a high-quality IBM under different conditions.
- Subjects
ARTIFICIAL neural networks; MACHINE learning; FEATURE extraction; GENETIC algorithms
- Publication
Information (2078-2489), 2023, Vol 14, Issue 7, p352
- ISSN
2078-2489
- Publication type
Article
- DOI
10.3390/info14070352