OMP-BASED MULTI-BAND SIGNAL RECONSTRUCTION FOR ECOLOGICAL SOUNDS RECOGNITION简

The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit （OMP）, which aims to develop a robust Ecological Sounds Recognition （ESR） system Firstly, the OMP is employed to sparsely decompose the original signal, thus the high correlation components are retained to reconstruct in the first stage. Then, according to the frequency distribution of both foreground sound and background noise, the signal can be compensated by the residual components in the second stage. Via the two-stage reconstruction, high non-stationary noises are ef- fectively reduced, and the reconstruction precision of foreground sound is improved. At recognition stage, we employ deep belief networks to model the composite feature sets extracted from reconstructed signal. The experimental results show that the proposed approach achieved superior recognition per- formance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio （SNR）, compared with the existing method.

Omp-based multi-band signal reconstruction for ecological sounds recognition

The paper proposes a new method of multi-band signal reconstruction based on Orthogonal Matching Pursuit (OMP), which aims to develop a robust Ecological Sounds Recognition (ESR) system. Firstly, the OMP is employed to sparsely decompose the original signal, thus the high correlation components are retained to reconstruct in the first stage. Then, according to the frequency distribution of both foreground sound and background noise, the signal can be compensated by the residual components in the second stage. Via the two-stage reconstruction, high non-stationary noises are effectively reduced, and the reconstruction precision of foreground sound is improved. At recognition stage, we employ deep belief networks to model the composite feature sets extracted from reconstructed signal. The experimental results show that the proposed approach achieved superior recognition performance on 60 classes of ecological sounds in different environments under different Signal-to-Noise Ratio (SNR), compared with the existing method.