We set Z and W space dimensions both to 128 for all our experiments across the two datasets. We also use only 4 mapping layers (as compared to 8 in the original StyleGAN2 paper). Further, we use the log-magnitude spectrogram representations generated using a Gabor transform(n_frames=256, stft_channels=512, hop_size=128), a Short-Time Fourier Transform (STFT) with a Gaussian window, to train the StyleGAN2 and the Phase Gradient Heap Integration (PGHI) for high-fidelity spectrogram inversion of textures to audio. For Greatest Hits dataset, we train the models for 2800kimgs with a batch size of 16, taking ~20 hours to train on a single RTX 2080 Ti GPU with 11GB memory. For Water Filling dataset, we train for 1400kimgs with the same batch size running for ~14 hours on the same GPU. The metrics for quality of the generated sounds in terms of Frechet Audio Distance (FAD) along with the StyleGAN2 code adapted for audio textures can be found below.

Table: StyleGAN2 Frechet Audio Distance (FAD)

Dataset	w-dim & z-dim	Number kimgs (iterations)	FAD Score
Greatest Hits Dataset	128	2800	0.51
Water Filling Dataset	128	1400	1.87

We use a RestNet-34 backbone as the architecture for our GAN inversion network. For both datasets, we train the Encoder for 1500 iterations with a batch size of 8 and choose the checkpoint with the lowest validation loss for inference. As in the original GAN Encoder paper we use an Adam optimizer with learning rate of 0.0001. Further, for the Water dataset, we apply a thresholding of -17db, i.e. we mask the frequency components with magnitude below -17db. For both datasets, the training took $\sim$ 25 hours to complete on a single GPU.

Table: GAN Inversion/Encoder (netE) Frechet Audio Distance (FAD)

Dataset	Number kimgs (iterations)	Gaver Sounds FAD Score	Real-World Sounds FAD Score
Greatest Hits Dataset	1500	4.73	0.586
Water Filling Dataset	1500	7.5	3.35

To model impact sounds, such as those in the Greatest Hits dataset, we use a combination of sounds synthesized using method 1 and 2. For method 1, we choose damping constant δ_n=0.001 for hard surfaces and δ_n=0.5 for soft surfaces. We provide variations in the generated sounds by using different impact surface sizes, φ and n (number of partials). We vary the first partial of ω between 60-240Hz for large impact surfaces and between 250-660Hz for smaller surfaces. For method 2, we vary the impulse width of each impact between 0.4 - 1.0 seconds to model scratches and between 0.1 - 0.4 seconds to model sharp hits. Further, we model dull sounds by configuring low frequency bands roughly between 10-1.5kHz and bright sounds using frequency bands above 4kHz. Water filling Gaver sounds are modelled as a combination of multiple impulses (modelled as individual water drops) concatenated with each other. We generate each drop using method 1 with an impulse width of 0.05 seconds. Each fill-level is controlled by linearly increasing or decreasing ω and its partials across the sound sample.

In all our experiments, we use 10 synthetic Gaver examples (5 per semantic attribute cluster) to generate the guidance vectors for controllable generation. Please see the for ablation studies section of this webpage for generating guidance vectors using different number of Gaver samples. Our code repository has all the Gaver configurations we used in our experiments.

We use a classifier based on this paper. For the classifier we use a DenseNet (with pre-training) based network, where the last layer is modified depending upon the number of classes we need. For binary re-scoring analysis the number of classes is 2 (to indicate presence of absence of the semantic attribute). The input to the classifier are 3 mel-spectrograms appended along the channel axis. As in the original paper, we follow this method to capture information at 3 different time scales, i.e. we compute mel-spectrogram of a signal using different window sizes and hop-lengths of [25ms, 10ms], [50ms, 25ms], and [100ms, 50ms] for each channel respectively. The different window sizes and hop-lengths ensure the network has different levels of information from the frequency and time domain on each channel. We use Adam optimizer while training with a learning rate of 0.0001, weight decay of 0.001 and train for 100 epochs.

To train the attribute re-scoring classifier, we manually curate and label a small dataset, from both Greatest Hits and Water Filling datasets. We use this classifier to quantitatively evaluate the effectiveness of our method in comparison with SeFa. For this, we manually curate approximately 250 samples of 2 seconds sounds for each semantic attribute from both the datasets. This manual curation involved visually analysing the video and auditioning the associated sound files to detect the semantic attribute being curated. For Perceived Brightness, we manually curated a set of bright sounds from hits made on dense material surfaces such as glass, tile, ceramic or metal to indicate the presence of the brightness attribute. And a set of dull or dark sounds made by impacts on soft materials such as cloth, carpet or paper to indicate an absence of the brightness attribute. For Rate we curated sound samples with just 1-2 impact sounds in a sample to indicate low-rate and all other samples as high-rate. For Impact Type, we curated a set of sound samples where the drumstick sharply hit the surface and another set of sounds where the drumstick scratched the surface. For Fill-Level for Water, we curated the sounds by sampling the first and last ~3 seconds of each file from the original dataset (of ~30 seconds length files) to indicate an empty bucket and a full bucket. These datasets are used to train attribute change or re-scoring classifiers during evaluation.