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학술대회 Harmonic and Percussive Source Separation Using a Convolutional Auto Encoder
Cited 8 time in scopus Download 1 time Share share facebook twitter linkedin kakaostory
저자
임우택, 이태진
발행일
201708
출처
European Signal Processing Conference (EUSIPCO) 2017, pp.1854-1858
DOI
https://dx.doi.org/10.23919/EUSIPCO.2017.8081520
협약과제
17HS2400, 다중소스 데이터 지능형 분석기반 고수준 정보추출 원천기술 연구, 유장희
초록
Real world audio signals are generally a mixture of harmonic and percussive sounds. In this paper, we present a novel method for separating the harmonic and percussive audio signals from an audio mixture. Proposed method involves the use of a convolutional auto-encoder on a magnitude of the spectrogram to separate the harmonic and percussive signals. This network structure enables automatic high-level feature learning and spectral domain audio decomposition. An evaluation was performed using professionally produced music recording. Consequently, we confirm that the proposed method provides superior separation performance compared to conventional methods.
키워드
Auto-Encoder, Convolutional Neural Networks, Deep Learning, Source Separation
KSP 제안 키워드
Audio signal, Auto-Encoder(AE), Conventional methods, Convolution neural network(CNN), Convolutional auto-encoder, Feature Learning, High-level features, Real-world, deep learning(DL), network structure, novel method