Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Audio Super-Resolution using Neural Networks
Volodymyr Kuleshov, S. Zayd Enam, Stefano Ermon
Feb 17, 2017 (modified: Mar 02, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:We propose a neural network-based technique for enhancing the quality of audio signals such as speech or music by transforming inputs encoded at low sampling rates into higher-quality signals with an increased resolution in the time domain. This amounts to generating the missing samples within the low-resolution signal in a process akin to image super-resolution. On standard speech and music datasets, this approach outperforms baselines at 2x, 4x, and 6x upscaling ratios. The method has practical applications in telephony, compression, and text-to-speech generation; it can also be used to improve the scalability of recently-proposed generative models of audio.
Conflicts:mcgill.ca, cornell.edu, berkeley.edu
Enter your feedback below and we'll get back to you as soon as possible.