This way, the gains will be returned as a numpy array of the size of your loudspeaker setup. All inactive speakers will have zero gain, so by the virtue of numpy broadcasting panning a mono signal is simply:import numpy as np noise = np.random.random((48000, 1)) # mono signal, of ...
as well as dump of sample data and ext files: dump_data = sim.load_real_data( dump_data.astype("float64"), 'data', mrt.get_inputs_ext()) model_root = path.join(dump_dir, model_name_tfm) np.save(path.join(model_root, "data.npy"), dump_data.astype('int8').asnumpy()) inf...
Even the smallest datasets are fairly large files, so this step will likely take a while. Select a dataset in the next cell, then run the next two cells, and go grab a snack and a cup of tea 😊 Alternatively, you can provide your own dataset in the form of a folder or gzip arch...