Abstract
Audio source separation is the process of decomposing a signal containing sounds from multiple sources into a set of signals, each from a single source. Source separation algorithms typically leverage assumptions about correlations between audio signal characteristics ('cues') and the audio sources or mixing parameters, and exploit these to do separation. We train a neural network to predict quality of source separation, as measured by Signal to Distortion Ratio, or SDR. We do this for three source separation algorithms, each leveraging a different cue-repetition, spatialization, and harmonicity/pitch proximity. Our model estimates separation quality using only the original audio mixture and separated source output by an algorithm. These estimates are reliable enough to be used to guide switching between algorithms as cues vary. Our approach for separation quality prediction can be generalized to arbitrary source separation algorithms.
Original language | English (US) |
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Title of host publication | 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 274-278 |
Number of pages | 5 |
ISBN (Electronic) | 9781538616321 |
DOIs | |
State | Published - Dec 7 2017 |
Event | 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 - New Paltz, United States Duration: Oct 15 2017 → Oct 18 2017 |
Publication series
Name | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics |
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Volume | 2017-October |
Other
Other | 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 |
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Country/Territory | United States |
City | New Paltz |
Period | 10/15/17 → 10/18/17 |
Funding
∗contributed equally †This work was supported by NSF Grant 1420971.
Keywords
- background
- foreground
- melody
- prediction
- repetition
- singing voice separation
- spatial
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science Applications