Earticle

초록

영어

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
2.1 NMF
2.2 Sound Event Detection
3. PROPOSED METHOD
3.1 Audio Processing
3.2 Adaptive NMF
3.3 Convolutional Neural network
3.4 System Flow
4. EXPERIMENT
5. CONCLUSION
REFERENCES

키워드

저자

• Bumsuk Jang [ CEO, BS SOFT Co., LTD., Gwangju, Korea ]
• Sang-Hyun Lee [ Assistant Professor, Department of Computer Engineering, Honam University, Gwangju, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380