# Non-Negative Matrix Factorization-Convolutional Neural Network (NMF-CNN) For Sound Event Detection

arXiv: https://arxiv.org/abs/2001.07874 · HTML: https://mlsanity.com/paper/2001.07874/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Diffusion-Based Feature Denoising and Using NNMF for Robust Brain Tumor Classification](https://mlsanity.com/paper/2603.13182/) (2026)
- [Optimizing Tensor Train Decomposition in DNNs for RISC-V Architectures Using Design Space Exploration and Compiler Optimizations](https://mlsanity.com/paper/2602.01996/) (2026)
- [Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification](https://mlsanity.com/paper/2603.29917/) (2026)

## Abstract (arXiv)

The main scientific question of this year DCASE challenge, Task 4 - Sound Event Detection in Domestic Environments, is to investigate the types of data (strongly labeled synthetic data, weakly labeled data, unlabeled in domain data) required to achieve the best performing system. In this paper, we proposed a deep learning model that integrates Non-Negative Matrix Factorization (NMF) with Convolutional Neural Network (CNN). The key idea of such integration is to use NMF to provide an approximate strong label to the weakly labeled data. Such integration was able to achieve a higher event-based F1-score as compared to the baseline system (Evaluation Dataset: 30.39% vs. 23.7%, Validation Dataset: 31% vs. 25.8%). By comparing the validation results with other participants, the proposed system was ranked 8th among 19 teams (inclusive of the baseline system) in this year Task 4 challenge.