1,721,050 research outputs found
Riconoscimento Automatico di Richieste di Aiuto e Comandi di Domotica per Ambient Assisted Living
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A distributed system for recognizing home automation commands and distress calls in the Italian language
No full textThis paper describes a system for recognizing distress calls and home automation voice commands in a smart-home. Distress calls are recognized with the purpose of assisting people in their own homes: when they are detected, a phone call is automatically established with a contact in a address book and the person can request for assistance. The voice call is established through a voice over ip stack, with hands-free communication guaranteed by an acoustic echo canceller. The acoustic environment is constantly monitored by several low-consuming devices distributed throughout the home. In each device, a voice activity detector detects speech segments, and a speech recognition engine recognizes commands and distress calls. Robustness to environmental disturbances has been increased by employing Power Normalized Cepstral Coefficients and by using an adaptive algorithm for interference cancellation. An Italian speech corpus of home automation commands and distress calls has been developed for evaluation purposes. The corpus has been recorded in a real room using multiple microphones, and each sentence has been uttered both in normal and shouted speaking styles. The system performance has been assessed in terms of commands/distress recognition accuracy in order to prove the effectiveness of the approach
COVID-19 vaccination and penile Mondor disease. There is any relationship?
Full text link: Dear Editor, the pandemic spread of Coronavirus 2 infection (SARS-CoV-2), determining the coronavirus disease 2019 (Covid-19), had devastating consequences globally with several waves affecting social and economic life. The use of masks, physical distancing, testing of exposed or symptomatic persons, contact tracing and isolation have helped limit the transmission where they have been rigorously applied; however, these actions have proved not sufficient to limit the virus spread [...]
Real-time implementation of robust PEM-AFROW based solutions for acoustic feedback control
No full textSigner Independent Isolated Italian Sign Recognition Based on Hidden Markov Models
No full textSign languages represent the most natural way to communicate for deaf and hard of hearing. However, there are often barriers between people using this kind of languages and hearing people, typically oriented to express themselves by means of oral languages. In order to facilitate the social inclu- siveness in everyday life for deaf minorities, technology can play an impor- tant role. Indeed many attempts have been recently made by the scientific community to develop automatic translation tools. Unfortunately, not many solutions are actually available for the Italian Sign Language (Lingua Italiana dei Segni - LIS) case study, specially for what concerns the recognition task. In this paper the authors want to face such a lack, in particular addressing the signer-independent case study, i.e., when the signers in the testing set are to included in the training set. From this perspective, the proposed algorithm represents the first real attempt in the LIS case. The automatic recognizer is based on Hidden Markov Models (HMMs) and video features have been extracted by using the OpenCV open source library. The effectiveness of the HMM system is validated by a comparative evaluation with Support Vector Machine approach. The video material used to train the recognizer and testing its performance consists in a database that the authors have deliberately cre- ated by involving ten signers and 147 isolated-sign videos for each signer. The database is publicly available. Computer simulations have shown the effective- ness of the adopted methodology, with recognition accuracies comparable to those obtained by the automatic tools developed for other sign languages
Unsupervised electric motor fault detection by using deep autoencoders
No full textFault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors ʼ knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron ( MLP ) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory ( LSTM ) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine ( OC-SVM ) algorithm. The performance has been evaluated in terms area under curve ( AUC ) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %
Enhanced Multichannel Histogram Equalization for Speech Recognition in noisy acoustic conditions
No full textFeature statistics normalization in the cepstral domain is one of the most performing approaches for robust automatic Speech Recognition (ASR) in noisy acoustic scenarios. According to this approach, feature coefficients are normalized by using suitable linear or nonlinear transformations in order to match the noisy speech statistics to the clean speech one. Histogram Equalization (HEQ) is an effective algorithm belonging to this category. Recently some of the authors have proposed an interesting extension to the HEQ original algorithm, in order to suitably deal with the multichannel audio information coming from multi-microphone sensory activity in far-field acoustic scenarios. In this paper the feature normalization capabilities of the multichannel HEQ technique are further enhanced by introducing the kernel estimation technique and employing the multi-condition training for ASR system parametrization. Computer simulations based on the Aurora 2 database have shown that a significant recognition improvement with respect to the single-channel counterpart and other multi-channel techniques can be achieved confirming the effectiveness of the idea
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