A Cloud-based On-line Disaggregation Algorithm for Home Appliance Loads

In this work, we address the problem of providing fast and on-line households appliance load detection in a non-intrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours. To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Articulatory-WaveNet: Deep Autoregressive Model for Acoustic-to-Articulatory Inversion

Acoustic-to-Articulatory Inversion, the estimation of articulatory kinematics from speech, is an important problem which has received significant attention in recent years. Estimated articulatory movements from such models can be used for many applications, including speech synthesis, automatic speech recognition, and facial kinematics for talking-head animation devices. Knowledge about the position of the articulators can also be extremely useful in speech therapy systems and Computer-Aided Language Learning (CALL) and Computer-Aided Pronunciation Training (CAPT) systems for second language learners. Acoustic-to-Articulatory Inversion is a challenging problem due to the complexity of articulation patterns and significant inter-speaker differences. This is even more challenging when applied to non-native speakers without any kinematic training data. This dissertation attempts to address these problems through the development of up-graded architectures for Articulatory Inversion. The proposed Articulatory-WaveNet architecture is based on a dilated causal convolutional layer structure that improves the Acoustic-to-Articulatory Inversion estimated results for both speaker-dependent and speaker-independent scenarios. The system has been evaluated on the ElectroMagnetic Articulography corpus of Mandarin Accented English (EMA-MAE) corpus, consisting of 39 speakers including both native English speakers and Mandarin accented English speakers. Results show that Articulatory-WaveNet improves the performance of the speaker-dependent and speaker-independent Acoustic-to-Articulatory Inversion systems significantly compared to the previously reported results