A hybrid technique for speech segregation and classification using a sophisticated deep neural network

6Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

© 2018 Qazi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Recent research on speech segregation and music fingerprinting has led to improvements in speech segregation and music identification algorithms. Speech and music segregation generally involves the identification of music followed by speech segregation. However, music segregation becomes a challenging task in the presence of noise. This paper proposes a novel method of speech segregation for unlabelled stationary noisy audio signals using the deep belief network (DBN) model. The proposed method successfully segregates a music signal from noisy audio streams. A recurrent neural network (RNN)-based hidden layer segregation model is applied to remove stationary noise. Dictionary-based fisher algorithms are employed for speech classification. The proposed method is tested on three datasets (TIMIT, MIR-1K, and MusicBrainz), and the results indicate the robustness of proposed method for speech segregation. The qualitative and quantitative analysis carried out on three datasets demonstrate the efficiency of the proposed method compared to the state-of-the-art speech segregation and classification-based methods.

Cite

CITATION STYLE

APA

Qazi, K. A., Nawaz, T., Mehmood, Z., Rashid, M., & Habib, H. A. (2018). A hybrid technique for speech segregation and classification using a sophisticated deep neural network. PLoS ONE, 13(3). https://doi.org/10.1371/journal.pone.0194151

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free