Animal-Independent Signal Enhancement Using Deep Learning

Type: BA thesis

Status: running

Date: July 15, 2022 - December 15, 2022

Supervisors: Christian Bergler, Andreas Maier, Elmar Nöth, Alexander Barnhill

Examining and segmenting bioacoustic signals is an essential part of biology. For example, by analysing orca calls it is possible to draw several conclusions regarding the animals’ communication and social behavior.1 However, to avoid having to manually go through hours of audio material to detect those calls, the so-called ORCA-SPOT toolkit was developed, which uses Deep Learning to separate relevant signals from pure ambient sounds.2 These may nevertheless still contain background noise, which makes the examination rather difficult. To remove this background noise, ORCA-CLEAN was developed. Again, using a Deep Learning approach by adapting the Noise2Noise concept, as well as using machine-generated binary masks as an additional attention mechanism, the orca calls are denoised as best as possible without requiring clean data as foundation.3
But, as mentioned, this toolkit is optimized for the denoising of orca calls. Marine biologists are of course not the only ones who require clean audio signals for their research. Ornithologists alone deal with a great variety of different noise. One that studies urban bird species would like city sounds to be filtered from his audio samples, whereas one who works with tropical birds rather wants his recordings clean from forest noise. One could argue that almost every biologist who analyses recordings of animal calls would have use for a denoising tool kit.
Another task where audio denoising is of great relevance is when interpreting and processing human speech. It can be used to improve the sound quality of a phone call or a video conference, to preprocess a voice command to a virtual assistant on a smartphone, to improve voice recognition software and many other examples. Even in medicine it can help when analysing pulmonary auscultative signals, which is the key method to detect and evaluate respiratory dysfunctions.4
It therefore makes sense to generalize ORCA-CLEAN and to make it trainable for other animal sounds, perhaps even human speech, or body noises. One would then have a generalized version of ORCA-CLEAN, which can then be trained according to the desired purpose. The goal of this thesis will be to describe and explain the respective changes in the code, as well as to evaluate how differently trained models perform on audio recordings of different animals. The transfer from a model specialized on orcas to one specialized on another animal species will be demonstrated using recordings of hyraxes. The data used contains tapes of 34 hyrax individuals. For each individual there are multiple tapes available, and for each tape there is a corresponding table containing information like the exact location, the length, the peak frequency or the call type for each call on the tape.
The hyrax is a small hoofed mammal of the family of Procaviidae.5, 6 They usually weigh 4 to 5 kg, are about 30 to 50 cm long and are mostly herbivorous.5 Their calls, especially the advertisement calls, are helpful for distinguishing different hyrax species and for analysing the animals’ behaviour.6
Here are a few rough approaches how I would realize this thesis. I would begin by modifying the ORCA-CLEAN code. Since orca calls very much differ from hyrax ones in terms of frequency range as well as in length, the prepocessing of the audio tapes would have to be modified. I would also like to add some more input/output spectrogram variants to the training process.
One could use pairs of noisy and denoised human speech samples for example, or a pure noise spectrogram versus a completely empty one. The probability with which each of these variants is chosen could additionally be made variable.
After that, I would train different models with hyrax audio tapes, including original ORCA-CLEAN as well as an the newly created adaptions, and evaluate their performance. Since the provided hyrax tapes aren’t all equally noisy, they can be sorted by the so-called Signal to Noise Ratio (SNR). One can then compare these values before and after denoising, e.g., by correlating them, and check if the files were denoised correctly or if relevant parts were removed.
With help of these results further alterations can be made, for example by changing the probabilities of the training methods or by adapting the hyperparameters of the deep network, until hopefully in the end, a suitable network which doesn’t require huge amount of data is the result.
I hope I was able to give some insight into what I imagine the subject to be, and how I would roughly execute it.
[2] C. Bergler, H. Schröter, R. X. Cheng, V. Barth, M. Weber, E. Nöth, H. Hofer, and A. Maier, “ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning” Scientific Reports, vol. 9, 12 2019.
[3] C. Bergler, M. Schmitt, A. Maier, S. Smeele, V. Barth, and E. Nöth, “ORCA-CLEAN: A Deep Denoising Toolkit for Killer Whale Communication“ Interspeech 2020 (pp. 1136-1140). International Speech Communication Association.
[4] F. Jin and F. Sattar “Enhancement of Recorded Respiratory Sound Using Signal Processing Techniques“ In A. Cartelli, M. Palma (Eds.) “Encyclopedia of Information Communication Technology” (pp. 291-300), 2009.