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This paper describes a low-power processor tailored for fast Fourier transform computations where transport triggering template is exploited. The processor is software-programmable while retaining an energy-efficiency comparable to existing fixed-function implementations. The power savings are achieved by compressing the computation kernel into one instruction word. The word is stored in an instruction loop buffer, which is more power-efficient than regular instruction memory storage.

Compressed sensing can be used to yield both compression and a limited form of security to the readings of sensors. This can be most useful when designing the low-resources sensor nodes that are the backbone of IoT applications. Here, we propose to use chaining of subsequent plaintexts to improve the robustness of CS-based encryption against ciphertext-only attacks, known-plaintext attacks and man-in-the-middle attacks.

Sound event detection is the task of identifying automatically the presence and temporal boundaries of sound events within an input audio stream. In the last years, deep learning methods have established themselves as the state-of-the-art approach for the task, using binary indicators during training to denote whether an event is active or inactive. However, such binary activity indicators do not fully describe the events, and estimating the envelope of the sounds could provide more precise modeling of their activity.

This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode de-

Data augmentation is crucial to improving the performance of deep neural networks by helping the model avoid overfitting and improve its generalization. In automatic speech recognition, previous work proposed several approaches to augment data by performing speed perturbation or spectral transformation. Since data augmented in these manners has similar acoustic representations with the original data, it has limited advantage in improving generalization of the acoustic model.

Speech is one of the primary means of communication for humans. It can be viewed as a carrier for information on several levels as it conveys not only the meaning and intention predetermined by a speaker, but also paralinguistic and extralinguistic information about the speaker’s age, gender, personality, emotional state, health state and affect. This makes it a particularly sensitive biometric, that should be protected.

Distributed estimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. The ambiguities are modelled by following a set-theoretic approach, that leads to each sensor employing a non-convex constraint set on the parameter vector. Consensus can be used to reach an estimate consistent with the measurements of all nodes, assuming that such an estimate exists, but unfortunately, such an approach leads to a non-convex problem.

Speech is one of the primary means of communication for humans. It can be viewed as a carrier for information on several levels as it conveys not only the meaning and intention predetermined by a speaker, but also paralinguistic and extralinguistic information about the speaker’s age, gender, personality, emotional state, health state and affect. This makes it a particularly sensitive biometric, that should be protected.