  The performance of speech and events recognition systems significantly
improved recently thanks to deep learning methods. However, some of these tasks
remain challenging when algorithms are deployed on robots due to the unseen
mechanical noise and electrical interference generated by their actuators while
training the neural networks. Ego-noise reduction as a preprocessing step
therefore can help solve this issue when using pre-trained speech and event
recognition algorithms on robots. In this paper, we propose a new method to
reduce ego-noise using only a microphone array and less than two minute of
noise recordings. Using Principal Component Analysis (PCA), the best covariance
matrix candidate is selected from a dictionary created online during
calibration and used with the Minimum Variance Distortionless Response (MVDR)
beamformer. Results show that the proposed method runs in real-time, improves
the signal-to-distortion ratio (SDR) by up to 10 dB, decreases the word error
rate (WER) by 55\% in some cases and increases the Average Precision (AP) of
event detection by up to 0.2.
