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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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A hybrid technique for speech segregation and classification using a sophisticated deep neural network
Khurram Ashfaq Qazi1, Tabassam Nawaz1, Zahid Mehmood1
1Department of Software Engineering, University of Engineering & Technology, Taxila, Pakistan.
Plos One
|March 21, 2018
Summary
This study introduces a new deep belief network (DBN) method for separating speech from noisy audio. The novel approach effectively isolates speech signals, even in challenging stationary noise conditions.
Area of Science:
- Signal Processing
- Machine Learning
- Audio Analysis
Background:
- Speech segregation and music identification are crucial for audio processing.
- Current methods face challenges with music segregation in noisy environments.
- Existing algorithms often require labeled data for effective performance.
Purpose of the Study:
- To propose a novel method for speech segregation in unlabelled, stationary noisy audio signals.
- To enhance the robustness of speech segregation algorithms.
- To address the limitations of current music segregation techniques.
Main Methods:
- Utilizing a deep belief network (DBN) model for speech segregation.
- Employing a recurrent neural network (RNN)-based hidden layer for stationary noise removal.
- Implementing dictionary-based Fisher algorithms for speech classification.
Main Results:
- Successfully segregates music signals from noisy audio streams.
- Demonstrates robustness in speech segregation across multiple datasets (TIMIT, MIR-1K, MusicBrainz).
- Outperforms state-of-the-art methods in qualitative and quantitative analyses.
Conclusions:
- The proposed DBN-based method offers an efficient solution for speech segregation in noisy conditions.
- The approach is effective for unlabelled, stationary noisy audio.
- This research advances the field of audio signal processing and machine learning applications.
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