상세검색
검색어 입력 검색어는 최대 5개까지 추가하실 수 있습니다.
비밀번호 변경 안내
비밀번호를 변경하신 지 90일 이상 지났습니다.
개인정보 보호를 위해 비밀번호를 변경해 주세요.
비밀번호 변경 안내
비밀번호를 변경하신 지 90일 이상 지났습니다.
개인정보 보호를 위해 비밀번호를 변경해 주세요.
- 학술저널
- 한국데이터정보과학회
- 한국데이터정보과학회지
- 한국데이터정보과학회지 제32권 제2호
- 2021.03
- 439 - 453 (15 pages)
- KCI우수등재
DOI : 10.7465/jkdi.2021.32.2.439
초록
We compare the effect of multiple input representations on polyphonic piano music transcription based on neural networks. A state-of-the-art piano transcription neural network model, onsets and frames, is explored. We first provide detailed backgrounds of the piano transcription and input representations for the readers who are unfamiliar with this area. For comparing their effects, we consider four spectrograms; Mel-spectrogram, Linear-spectrogram, Log-spectrogram and constant-Q-transform with various hyper parameters. The effects of frequency bins, Short Time Fourier Transformation (STFT) window size and hop length on the four spectrograms are also examined. Our results show that Mel-spectrogram of 2,048 STFT window size, 512 frequency bins and 256 hop length yields the highest accuracy. We show that Mel-spectrogram is one of the most satisfactory input representations in general. Mel-spectrogram dominates other spectrograms and keeps a relatively high transcription accuracy even at the low resolutions in our experiments.
목차
Abstract
1. Introduction
2. Background of piano music transcription
3. Background of audio input representations
4. Onsets and frames model
5. Application to real data
6. Concluding remarks
References
리뷰(0)
도움이 되었어요.0
도움이 안되었어요.0
도움이 되었어요.0
도움이 안되었어요.0
