Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of the appliances using electrical parameters acquired from a single metering point. State-of-the-art approaches are based on deep neural networks, and for training, they require a significant amount of data annotated at the sample level, defined as strong labels. This paper presents an appliance classification method based on a Convolutional Recurrent Neural Network trained with weak supervision. Learning is formulated as a Multiple-Instance Learning problem, and the network is trained on labels provided for an entire segment of the aggregate power, defined as weak labels. Weak labels are coarser annotations that are intrinsically less costly to obtain compared to strong labels. An extensive experimental evaluation has been conducted on the UK-DALE and REFIT datasets comparing the proposed approach to three benchmark methods. The results obtained for different amounts of strongly and weakly labeled data and mixing UK-DALE and REFIT confirm the effectiveness of weak labels compared to fully supervised and semi-supervised benchmarks methods.

A Speaker Localization algorithm based on Neural Networks for multi-room domestic scenarios is proposed in this paper. The approach is fully data-driven and employs a Neural Network fed by GCC-PHAT (Generalized Cross Correlation Phase Transform) Patterns, calculated by means of the microphone signals, to determine the speaker position in the room under analysis. In particular, we deal with a multi-room case study, in which the acoustic scene of each room is influenced by sounds emitted in the other rooms. The algorithm is tested against the home recorded DIRHA dataset, characterized by multiple wall and ceiling microphone signals for each room. In particular, we focused on the speaker localization problem in two distinct neighbouring rooms. We assumed the presence of an Oracle multi-room Voice Activity Detector (VAD) in our experiments. A three-stage optimization procedure has been adopted to find the best network configuration and GCC-PHAT Patterns combination. Moreover, an algorithm based on Time Difference of Arrival (TDOA), recently proposed in literature for the addressed applicative context, has been considered as term of comparison. As result, the proposed algorithm outperforms the reference one, providing an average localization error, expressed in terms of RMSE, equal to 525 mm against 1465 mm. Concluding, we also assessed the algorithm performance when a real VAD, recently proposed by some of the authors, is used. Even though a degradation of localization capability is registered (an average RMSE equal to 770 mm), still a remarkable improvement with respect to the state of the art performance is obtained.