This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of them separately in the initial stages. We show that instead of concatenating the features of each channel into a single feature vector the network learns sound events in multichannel audio better when they are presented as separate layers of a volume. Using the proposed spatial features over monaural features on the same network gives an absolute F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and 2.7% on the TUT-SED 2009 dataset that is fifteen times larger.

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The utilization of distributed microphone arrays in many speech processing applications such as beamforming and speaker localization rely on the precise knowledge of microphone locations. Several selflocalization approaches have been presented in the literature but still a simple, accurate, and robust method for asynchronous devices is lacking. This work presents an analytical solution for estimating the positions and rotations of asynchronous loudspeaker equipped microphone arrays or devices. The method is based on emitting and receiving calibration signals from each device, and extracting the time of arrival (TOA) values. Utilizing the knowledge of array geometry in the TOA estimation is proposed to improve accuracy of translation. Results with measurements using four devices on a table surface demonstrates a mean translation error of 11 mm with standard deviation of 6 mm and mean z-axis rotation error of 0.11 (rad) with a standard deviation of 0.14 (rad) in contrast to computer vision annotations with 200 rotations and translation estimates.

Speech separation algorithms are faced with a difficult task of producing high degree of separation without containing unwanted artifacts. The time-frequency (T-F) masking technique applies a real-valued (or binary) mask on top of the signal's spectrum to filter out unwanted components. The practical difficulty lies in the mask estimation. Often, using efficient masks engineered for separation performance leads to presence of unwanted musical noise artifacts in the separated signal. This lowers the perceptual quality and intelligibility of the output.Microphone arrays have been long studied for processing of distant speech. This work uses a feed-forward neural network for mapping microphone array's spatial features into a T-F mask. Wiener filter is used as a desired mask for training the neural network using speech examples in simulated setting. The T-F masks predicted by the neural network are combined to obtain an enhanced separation mask that exploits the information regarding interference between all sources. The final mask is applied to the delay-and-sum beamformer (DSB) output.The algorithm's objective separation capability in conjunction with the separated speech intelligibility is tested with recorded speech from distant talkers in two rooms from two distances. The results show improvement in instrumental measure for intelligibility and frequency-weighted SNR over complex-valued non-negative matrix factorization (CNMF) source separation approach, spatial sound source separation, and conventional beamforming methods such as the DSB and minimum variance distortionless response (MVDR).

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