Voice signals are monitored by deployed microphone array in a low-power state. Time-delay features of each microphone pair are extracted to estimate the direction of the sound source, which can be combined with the mobility of autonomous robot. Before usage, we first train a NN model using supervised learning, on dataset collected from GSound simulator (Schissler and Manocha, 2011). Trained NN model is then fine-tuned through online RL, in daily usage.

baseline: traditional TDOA methos based on Geometric Features

gcc: extracted GCC features

main_ssl: filed test codes for walker

map: point cloud generated by mapping module

online_wav: ssl temp storage

save: pre-trained model

wakeup: keyword spotting, choose 1 second of raw audio as input signal. In particular, 40 MFCC features are extracted from a frame of length 40ms with a stride of 20ms, which gives 1960 (40×49) features for each 1-second audio clip

wav: raw audio data

game_multi.py: simulated environment to perform online tuning

markov_loc.py: combine RL with markov localization

train.py: pre-training code