TY - GEN
T1 - VoiceBit
T2 - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
AU - Chen, Gang
AU - Zhou, Zhaoheng
AU - He, Shengyu
AU - Zheng, Yi
AU - Yi, Wang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In mobile speech communication, the speech quality can be severely degraded when the mobile device users are in a noisy acoustic environment. To suppress environmental noises, deep learning based monaural speech separation methods have achieved remarkable progress on boosting the performance of the separation accuracy. However, the latency and computational cost of these methods remain far insufficient for mobile devices. Performance and power constraints make it still challenging to deploy such methods on mobile devices due to their high computational complexity. In this paper, we present VoiceBit, an efficient and light-weight human voice separation framework for real-time speech sep-aration on mobile devices. Specifically, we propose a light-weight speech separation network with reduced computation complexity and memory footprint for minimal compromise in accuracy, to segregate human voice and interfering noises directly from time-domain signals. Furthermore, we present a set of parallel optimizations to accelerate the operations in VoiceBit. Our experiment results show that VoiceBit achieves significant speedup and energy efficiency compared with state-of-the-art frameworks.
AB - In mobile speech communication, the speech quality can be severely degraded when the mobile device users are in a noisy acoustic environment. To suppress environmental noises, deep learning based monaural speech separation methods have achieved remarkable progress on boosting the performance of the separation accuracy. However, the latency and computational cost of these methods remain far insufficient for mobile devices. Performance and power constraints make it still challenging to deploy such methods on mobile devices due to their high computational complexity. In this paper, we present VoiceBit, an efficient and light-weight human voice separation framework for real-time speech sep-aration on mobile devices. Specifically, we propose a light-weight speech separation network with reduced computation complexity and memory footprint for minimal compromise in accuracy, to segregate human voice and interfering noises directly from time-domain signals. Furthermore, we present a set of parallel optimizations to accelerate the operations in VoiceBit. Our experiment results show that VoiceBit achieves significant speedup and energy efficiency compared with state-of-the-art frameworks.
UR - https://www.scopus.com/pages/publications/85152224305
U2 - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00296
DO - 10.1109/HPCC-DSS-SmartCity-DependSys57074.2022.00296
M3 - 会议稿件
AN - SCOPUS:85152224305
T3 - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
SP - 1987
EP - 1994
BT - Proceedings - 24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 December 2022 through 20 December 2022
ER -
