October 25, 2020
We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working directly on the raw waveform.
Publisher
InterSpeech
April 22, 2024
Vasu Sharma *, Karthik Padthe *, Newsha Ardalani, Kushal Tirumala, Russ Howes, Hu Xu, Bernie Huang, Daniel Li (FAIR), Armen Aghajanyan, Gargi Ghosh, Luke Zettlemoyer
April 22, 2024
April 14, 2024
Heng-Jui Chang, Ning Dong (AI), Ruslan Mavlyutov, Sravya Popuri, Andy Chung
April 14, 2024
April 05, 2024
Suyu Ge, Chunting Zhou, Rui Hou, Madian Khabsa, Yi-Chia Wang, Qifan Wang, Jiawei Han, Yuning Mao
April 05, 2024
March 05, 2024
Alex Liu, Matt Le, Apoorv Vyas, Bowen Shi, Andros Tjandra, Wei-Ning Hsu
March 05, 2024
Product experiences
Foundational models
Product experiences
Latest news
Foundational models