Tempo and genre are two inter-leaved aspects of music, genres are often associated to rhythm patterns which are played in specific tempo ranges. In this paper, we focus on the recent Deep Rhythm system based on a harmonic representation of rhythm used as an input to a convolutional neural network. To consider the relationships between frequency bands, we process complex-valued inputs through complexconvolutions. We also study the joint estimation of tempo/genre using a multitask learning approach. Finally, we study the addition of a second input branch to the system based on a VGG-like architecture applied to a mel-spectrogram input. This multi-input approach allows to improve the performances for tempo and genre estimation.