Multiple Noise Power Spectrum Estimation Algorithms for Speech Enhancement
- Login to Download
- 1 Credits
Resource Overview
This implementation includes various noise power spectrum estimation algorithms designed for speech enhancement applications, utilizing standard spectral subtraction as the primary methodology. The framework serves as a comparative platform for evaluating different noise estimation techniques, with comprehensive details provided in the readme.txt file. Key features include implementations of minimum statistics, time-recursive averaging, and voice activity detection-based approaches.
Detailed Documentation
This paper presents multiple noise power spectrum estimation algorithms applied to speech enhancement systems. The implementation employs standard spectral subtraction as the core processing method, where the clean speech spectrum is estimated by subtracting the noise spectrum from the noisy input. The primary objective of this work is to provide a comparative analysis framework for various noise estimation algorithms, including their computational efficiency and enhancement performance. The system architecture features modular implementations allowing easy integration and testing of different estimation methods. Detailed algorithm specifications, parameter configurations, and performance metrics are documented in the readme.txt file. Each algorithm is implemented with optimized windowing functions and Fourier transform operations to ensure real-time processing capabilities.
- Login to Download
- 1 Credits