TY - GEN
T1 - Acoustic Scene Classification for Bone-Conducted Sound Using Transfer Learning and Feature Fusion
AU - Bi, Sijun
AU - Xu, Liang
AU - Zhao, Shenghui
AU - Wang, Jing
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The air-conducted (AC) sound is usually used in the task of acoustic scene classification (ASC). Compared with the AC sound, bone-conducted (BC) sound has the unique advantage of shielding background noise. However, the amount of information contained in BC sound is far less than that in the AC sound due to its limited frequency bandwidth. In this paper, an acoustic scene classification method for BC sound is proposed with a small BC dataset. Firstly, the prosodic features are combined with the spectral features to capture more information, and feature fusion is adopted. Secondly, in order to deal with the small BC dataset, transfer learning is used with a large AC dataset. Finally, a deep learning network based on local residual learning is proposed. The experimental results show that the proposed method achieves the superior performance over the reference models.
AB - The air-conducted (AC) sound is usually used in the task of acoustic scene classification (ASC). Compared with the AC sound, bone-conducted (BC) sound has the unique advantage of shielding background noise. However, the amount of information contained in BC sound is far less than that in the AC sound due to its limited frequency bandwidth. In this paper, an acoustic scene classification method for BC sound is proposed with a small BC dataset. Firstly, the prosodic features are combined with the spectral features to capture more information, and feature fusion is adopted. Secondly, in order to deal with the small BC dataset, transfer learning is used with a large AC dataset. Finally, a deep learning network based on local residual learning is proposed. The experimental results show that the proposed method achieves the superior performance over the reference models.
KW - Bone-conducted sound
KW - feature fusion
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85149975182&partnerID=8YFLogxK
U2 - 10.1109/ICICSP55539.2022.10050618
DO - 10.1109/ICICSP55539.2022.10050618
M3 - Conference contribution
AN - SCOPUS:85149975182
T3 - 2022 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
SP - 519
EP - 522
BT - 2022 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Communication and Signal Processing, ICICSP 2022
Y2 - 26 November 2022 through 28 November 2022
ER -