Update README.md
Browse files
README.md
CHANGED
@@ -42,6 +42,20 @@ issn = {2050-084X},
|
|
42 |
publisher = {eLife Sciences Publications, Ltd},
|
43 |
}
|
44 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
45 |
```
|
46 |
|
47 |
## Contact
|
|
|
42 |
publisher = {eLife Sciences Publications, Ltd},
|
43 |
}
|
44 |
|
45 |
+
```
|
46 |
+
```
|
47 |
+
@article {Gu2023.09.30.560270,
|
48 |
+
author = {Nianlong Gu and Kanghwi Lee and Maris Basha and Sumit Kumar Ram and Guanghao You and Richard Hahnloser},
|
49 |
+
title = {Positive Transfer of the Whisper Speech Transformer to Human and Animal Voice Activity Detection},
|
50 |
+
elocation-id = {2023.09.30.560270},
|
51 |
+
year = {2023},
|
52 |
+
doi = {10.1101/2023.09.30.560270},
|
53 |
+
publisher = {Cold Spring Harbor Laboratory},
|
54 |
+
abstract = {This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.Competing Interest StatementThe authors have declared no competing interest.},
|
55 |
+
URL = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270},
|
56 |
+
eprint = {https://www.biorxiv.org/content/early/2023/10/02/2023.09.30.560270.full.pdf},
|
57 |
+
journal = {bioRxiv}
|
58 |
+
}
|
59 |
```
|
60 |
|
61 |
## Contact
|