Biodenoising

We published the arxiv preprint of our paper on denoising animal vocalizations without clean data.

Speech enhancement e.g. denoising speech signals, currently requires large amounts of isolated speech. This is not the case for animal vocalizations. Data recorded in the wild is often noisy, and even biologgers capture self-generated and wind noise. In the lab, self-noise or fan noise are often present.

How can we denoise animal vocalizations without clean data? The current research relies on estimating the noisy targets in noisier mixture i.e. creating mixtures by adding more noise on top of these targets. However, speech enhancement models already know a lot about patterns in audio time series. So why not use these models to obtain pseudo-clean targets instead of using the noisy targets.

The paper has the following contributions:

• We introduce a new benchmarking dataset comprising clean vocalizations from different taxa and noise samples.
• We introduce a separate training set comprising noisy vocalization from existing bioacoustic datasets and noise samples from environmental sounds datasets. The training data can be downloaded and processed using a Python library biodenoising-datasets.
• We propose a methodology to leverage speech enhancement models to denoise animal vocalizations without clean data. The experiments can be reproduced using the Python library biodenoising.

One important thing, in our experiments we discovered that time scaling e.g. slowing down or speeding up the vocalization can help. Why does this transformation makes sense? We know that with some exceptions the pitch of a vocalization is correlated with the size of the its body. Check out the video below to see how this works for whales and birds.

If we can bring up the pitch within the human speech range, we could re-use speech enhancement models. Speech enhancement models learn signal priors such as sparsity, structure of vowels and phonemes that may be useful when denoising animal vocalizations.

Bibtex