Appendix 1—table 3. Comparison of feature sets on the downstream task of predicting mouse identity given acoustic features of single syllables.
Classification accuracy, in percent, averaged over five disjoint, class-balanced splits of the data is reported. A class-weighted log-likelihood loss is targeted to help correct for class imbalance. Empirical standard deviation is shown in parentheses. Each MUPET acoustic feature is independently z-scored as a preprocessing step. Latent feature principal components are truncated when >99% of the feature variance is explained. The multilayer perceptron (MLP) classifiers are two-layer networks with a hidden layer size of 100, ReLU activations, and an L2 weight regularization parameter ‘alpha,’ trained with ADAM optimization with a learning rate of 10-3 for 200 epochs. Chance performance is 2.8% for top-1 accuracy and 13.9% for top-5 accuracy. D denotes the dimension of each feature set, with Gaussian random projections used to decrease the dimension of spectrograms.
| Predicting mouse identity (Figure 4f) | |||||
|---|---|---|---|---|---|
| Spectrogram | MUPET | Latent | |||
| D = 10 | D = 30 | D = 100 | D = 9 | D = 8 | |
| Top-1 accuracy | |||||
| MLP (α = 0.01) | 9.9 (0.2) | 14.9 (0.2) | 20.4 (0.4) | 14.7 (0.2) | 17.0 (0.3) |
| MLP (α = 0.001) | 10.8 (0.1) | 17.3 (0.4) | 25.3 (0.3) | 19.0 (0.3) | 22.7 (0.5) |
| MLP (α = 0.0001) | 10.7 (0.2) | 17.3 (0.3) | 25.1 (0.3) | 20.6 (0.4) | 24.0 (0.2) |
| Top-5 accuracy | |||||
| MLP (α = 0.01) | 36.6 (0.4) | 45.1 (0.5) | 55.0 (0.3) | 46.5 (0.3) | 49.9 (0.4) |
| MLP (α = 0.001) | 38.6 (0.2) | 50.7 (0.6) | 62.9 (0.4) | 54.0 (0.2) | 59.2 (0.6) |
| MLP (α = 0.0001) | 38.7 (0.5) | 50.8 (0.3) | 63.2 (0.4) | 57.3 (0.4) | 61.6 (0.4) |