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Approaching an unknown communication system by latent space exploration and causal inference

Gašper Beguš, Andrej Leban, Shane Gero

TL;DR

The paper presents a model-agnostic approach to probing unknown communication systems by pairing latent-space exploration in an unsupervised generative model with causal-inference estimators (CDEV). By manipulating latent encodings to extreme values and testing their impact on observable outputs, the authors identify meaningful latent factors, such as the number of clicks and spectral properties, in sperm whale codas. They demonstrate that bit 1 primarily encodes the number of clicks, bit 0 encodes spectral mean, and bit 3 encodes spectral regularity, with incremental causal effects supporting these interpretations. The methodology is validated on.raw acoustic data from sperm whale codas and shown to reproduce known patterns while also uncovering new acoustic properties, offering a generalizable tool for interpreting complex, poorly understood communication systems and extending to other architectures and datasets.

Abstract

This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this method yields insights for model interpretability. With this, we can test for what properties of unknown data the model encodes as meaningful, using it to glean insight into the communication system of sperm whales (Physeter macrocephalus), one of the most intriguing and understudied animal communication systems. The network architecture used has been shown to learn meaningful representations of speech; here, it is used as a learning mechanism to decipher the properties of another vocal communication system in which case we have no ground truth. The proposed methodology suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time. We also argue that our models uncover rules that govern the structure of units in the communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal inference methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space. Finally, the proposed approach can be extended to other architectures and datasets.

Approaching an unknown communication system by latent space exploration and causal inference

TL;DR

The paper presents a model-agnostic approach to probing unknown communication systems by pairing latent-space exploration in an unsupervised generative model with causal-inference estimators (CDEV). By manipulating latent encodings to extreme values and testing their impact on observable outputs, the authors identify meaningful latent factors, such as the number of clicks and spectral properties, in sperm whale codas. They demonstrate that bit 1 primarily encodes the number of clicks, bit 0 encodes spectral mean, and bit 3 encodes spectral regularity, with incremental causal effects supporting these interpretations. The methodology is validated on.raw acoustic data from sperm whale codas and shown to reproduce known patterns while also uncovering new acoustic properties, offering a generalizable tool for interpreting complex, poorly understood communication systems and extending to other architectures and datasets.

Abstract

This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this method yields insights for model interpretability. With this, we can test for what properties of unknown data the model encodes as meaningful, using it to glean insight into the communication system of sperm whales (Physeter macrocephalus), one of the most intriguing and understudied animal communication systems. The network architecture used has been shown to learn meaningful representations of speech; here, it is used as a learning mechanism to decipher the properties of another vocal communication system in which case we have no ground truth. The proposed methodology suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time. We also argue that our models uncover rules that govern the structure of units in the communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal inference methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space. Finally, the proposed approach can be extended to other architectures and datasets.
Paper Structure (27 sections, 8 equations, 19 figures, 6 tables)

This paper contains 27 sections, 8 equations, 19 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Sperm whale communication
  3. Related work
  4. The architecture used and the CDEV methodology
  5. The experiment from the continuous-treatment causal inference point of view
  6. An introduction to the methodology: the number of clicks
  7. Click spacing and regularity
  8. Acoustic properties
  9. Conclusion
  10. Appendix
  11. Code repository, reproducibility, and robustness
  12. Robustness to encoding size and model initialization: results obtained from a separate model with a six-bit encoding
  13. An estimate for the accuracy of the estimation: the number of clicks ATE obtained from the acoustic data
  14. Details regarding the architecture
  15. The data
  16. ...and 12 more sections

Figures (19)

  • Figure 1: Examples of real codas. Left: type 1+1+3 with 5 clicks, right: type 9R with 9 clicks. The 9R type was never part of the training data, yet our network learns to generate codas that resemble this type (Sec. \ref{['sec:regularity']}).
  • Figure 2: Model and approach overview.
  • Figure 3: Example of the application of the click detector on noisy generated data. The minor peaks are artifacts picked up in training due to the data not being free of whale dialogue, the red vertical lines are the detected clicks, and the yellow horizontal line is the inferred volume level for the vocalization to be coming from the primary whale.
  • Figure 4: Top: The ATE for the number of clicks with the baseline chosen as left: at $t'=-1$, right: at $t'=1$. Bottom: The ATE for the standard deviation in the number of clicks for the baseline at $t'=-1$.
  • Figure 5: Left: Bit 1, right: bit 2. Top:Mean ICI ATE, stratified by the number of clicks. Bottom: Coda regularity ATE, stratified by the number of clicks. The training range is denoted by dashed lines, with the baseline set at $t'=-1$ for all.
  • ...and 14 more figures