Tampere University Rotated Circular Array Dataset

Advancements in deep learning have resulted in new techniques to address sophisticated audio processing tasks, such as sound localization and recognition. However, supervised training of deep neural networks (DNNs) requires a significant amount of training data. Existing datasets are either recorded or allow synthetic recordings through impulse responses (IRs) via convolution. Recorded datasets often lack sufficient and versatile material for supervised DNN training. On the other hand, impulse response databases allow large scale dataset creation, provided that suitable IRs are available. However, existing IR datasets do not cater to the data requirements of moving and crossing sources problem in sound localization, due to insufficient angular resolution. This work introduces a versatile room IR dataset to address this problem. Various diverse environments such as office rooms, meeting rooms, corridor, and an anechoic chamber are chosen for the data collection. The chosen rooms have varying characteristics, such as reverberation times (T60) and volumes. The data is collected by placing the speaker at three different distances from a rotated microphone array, thus mimicking the moving source condition. Direction of arrival (DoA) estimation is performed by spatializing the sound signal with the collected IRs to verify their quality. The dataset will be publicly available.