DOI : https://doi.org/10.18517/ijaseit.11.2.12833

Music Source Separation Using ASPP Based on Coupled U-Net Model

Suwon Yang (1), Daewon Lee (2)
(1) Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea
(2) Department of Computer Engineering, Seokyeong University, 124 Seogyeong-ro Seongbuk-gu, Seoul, 02173, Korea
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How to cite (IJASEIT) :
Yang, Suwon, and Daewon Lee. “Music Source Separation Using ASPP Based on Coupled U-Net Model”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, Apr. 2021, pp. 589-94, doi:10.18517/ijaseit.11.2.12833.
Citation Format :
Noise has established itself as one of the factors that interfere with modern human life, and various noise canceling techniques have been studied to prevent noise. While the old era's noise-canceling technique focused on the physical soundproofing technique, multiple studies have been conducted on the active noise canceling technique that removes only the activated noise in the current era. Active noise canceling (ANC) or digital noise-canceling technology is based on the sound source separation method. This leads to sound source separation technology, which refers to the technology to separate individual sound signals from mixture sounds. Most of the source separation technologies focus on improving speech, not noise reduction. This technology makes it possible to obtain desired sound information more accurately and further improves noise-canceling technology by eliminating unwanted sound information. To provide deeper capability and more enhanced sound separation than the existing structure, we are focused on coupled U-Net model and Atrous spatial pyramid pooling technique (ASPP). This paper presents the music source separation method that combined Coupled U-Net structure with Atrous spatial pyramid pooling technique. To prove the proposed source separation method, we compared GNSDR, GSIR, and GSAR using MIR-1K, a data set that can evaluate the performance of the music source separation. Performance results show that the proposed source separation method overcame other methods' disadvantages and strengthened the feature map.

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