BROWSE
Detail
Conference Paper
Harmonic and Percussive Source Separation Using a Convolutional Auto Encoder
- Authors
- Wootaek Lim, Taejin Lee
- Issue Date
- 2017-08
- Citation
- European Signal Processing Conference (EUSIPCO) 2017, pp.1854-1858
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Audio signal, Auto-Encoder(AE), Conventional methods, Convolutional auto-encoder, Network structure, Real-world, feature learning, high-level feature, novel method, separation performance, source separation
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
