Detection of activity and position of speakers by using deep neural networks and acoustic data augmentation

The task of Speaker LOCalization (SLOC) has been the focus of numerous works in the research field, where SLOC is performed on pure speech data, requiring the presence of an Oracle Voice Activity Detection (VAD) algorithm. Nevertheless, this perfect working condition is not satisfied in a real world scenario, where employed VADs do commit errors. This work addresses this issue with an extensive analysis focusing on the relationship between several data-driven VAD and SLOC models, finally proposing a reliable framework for VAD and SLOC. The effectiveness of the approach here discussed is assessed against a multi-room scenario, which is close to a real-world environment. Furthermore, up to the authors’ best knowledge, only one contribution proposes a unique framework for VAD and SLOC acting in this addressed scenario; however, this solution does not rely on data-driven approaches. This work comes as an extension of the authors’ previous research addressing the VAD and SLOC tasks, by proposing numerous advancements to the original neural network architectures. In details, four different models based on convolutional neural networks (CNNs) are here tested, in order to easily highlight the advantages of the introduced novelties. In addition, two different CNN models go under study for SLOC. Furthermore, training of data-driven models is here improved through a specific data augmentation technique. During this procedure, the room impulse responses (RIRs) of two virtual rooms are generated from the knowledge of the room size, reverberation time and microphones and sources placement. Finally, the only other framework for simultaneous detection and localization in a multi-room scenario is here taken into account to fairly compare the proposed method. As result, the proposed method is more accurate than the baseline framework, and remarkable improvements are specially observed when the data augmentation techniques are applied for both the VAD and SLOC tasks

ANALISI DI ETEROPLASMIE MEDIANTE DHPLC IN UN CASO DI IDENTIFICAZIONE PERSONALE

Il sequenziamento del DNA mitocondriale (mtDNA) è un valido approccio per la tipizzazione dei campioni biologici degradati caratterizzati da uno scarso contenuto di DNA genomico. Ci sono diversi aspetti analitici da considerare nella validazione dei risultati ottenuti mediante analisi dell’mtDNA. Una delle problematiche è connessa al fenomeno dell’eteroplasmia che è quella condizione in cui due o più aplotipi sono presenti in un singolo individuo, cellula o mitocondrio. Le eteroplasmie possono essere di sequenza e di lunghezza. Quest’ultime rendono difficile l’interpretazione dei risultati in quanto determinano uno slittamento del modulo di lettura della sequenza a valle del punto eteroplasmico. Inoltre il sequenziamento diretto, la metodica di prima scelta nell’analisi forense dell’mtDNA, non permette di determinare la lunghezza della variazione nucleotidica delle molecole eteroplasmiche. Nell’identificazione forense la presenza di eteroplasmie influenza l’eventuale corrispondenza tra il campione biologico in esame ed i putativi parenti materni in quanto gli aplotipi a confronto sembrano differire. In questo studio descriviamo la positiva identificazione di un resto scheletrico, il cui profilo mitocondriale presentava un’eteroplasmia di lunghezza, che è stato comparato con i profili ottenuti rispettivamente dalla madre e dal fratello. L’eteroplasmia di lunghezza è stata risolta mediante analisi di cromatografia liquida denaturante ad alta pressione (DHPLC) che ha permesso di discriminare le molecole eteroplasmiche sulla base della loro differenza di lunghezza. La DHPLC è una metodologia largamente utilizzata nel campo della ricerca biologica per rilevare mutazioni genetiche, ma l’estrema sensibilità e le ampie possibilità d’applicazione nell’analisi del DNA la rendono uno strumento utile nel campo dell’identificazione genetica forense

Analysis of distribution transformers under fault conditions for determination of transient recovery voltage

Abstract: The analysis of distribution trans- formers under short-circuit conditions is presented to determine the transient recovery voltage that appears across the poles of the breaking device at the current extinction. The frequency response of the transformer is determined and an equivalent circuit is derived. A simplified circuit, suitable for laboratory tests, is obtained from the equivalent circuit

Detection of activity and position of speakers by using deep neural networks and acoustic data augmentation

The task of Speaker LOCalization (SLOC)has been the focus of numerous works in the research field, where SLOC is performed on pure speech data, requiring the presence of an Oracle Voice Activity Detection (VAD)algorithm. Nevertheless, this perfect working condition is not satisfied in a real world scenario, where employed VADs do commit errors. This work addresses this issue with an extensive analysis focusing on the relationship between several data-driven VAD and SLOC models, finally proposing a reliable framework for VAD and SLOC. The effectiveness of the approach here discussed is assessed against a multi-room scenario, which is close to a real-world environment. Furthermore, up to the authors’ best knowledge, only one contribution proposes a unique framework for VAD and SLOC acting in this addressed scenario; however, this solution does not rely on data-driven approaches. This work comes as an extension of the authors’ previous research addressing the VAD and SLOC tasks, by proposing numerous advancements to the original neural network architectures. In details, four different models based on convolutional neural networks (CNNs)are here tested, in order to easily highlight the advantages of the introduced novelties. In addition, two different CNN models go under study for SLOC. Furthermore, training of data-driven models is here improved through a specific data augmentation technique. During this procedure, the room impulse responses (RIRs)of two virtual rooms are generated from the knowledge of the room size, reverberation time and microphones and sources placement. Finally, the only other framework for simultaneous detection and localization in a multi-room scenario is here taken into account to fairly compare the proposed method. As result, the proposed method is more accurate than the baseline framework, and remarkable improvements are specially observed when the data augmentation techniques are applied for both the VAD and SLOC tasks

A NEW HYPOTHESIS ON THE ORIGIN OF “TOUCH DNA”

The possibility to detect biological traces belonging to the individual responsible of a crime at the crime scene has always been a fundamental target of forensic investigation. The propensity to leave behind genetic material through contact (“touch DNA”) has been demonstrated to genetically differ among individuals depending upon the specificity of each individual’s skin. So far, these DNA traces have been assumed to originate from the keratinocytes sloughed off the upper epidermal layers, resulting in the rather simplistic definition of two categories of individuals, the so-called “good shedders” and “poor shedders”, depending on the degree of their skin’s propensity to leave DNA traces. The assumption that the propensity to leave behind genetic material reflects the shedding of keratinocytes is conventionally taken for granted, hence has dominated the field despite the lack of any solid scientific evidence. Only recently this assumption on the source of skin-derived “touch DNA” has started to be questioned, prompting experimental investigation. The correct identification of the biological samples under analysis is crucial in forensic investigation in that it represents the pivotal issue attesting that the resulting genetic profiles are fully reliable in terms of weight of the evidence, and to avoid the risk of erroneously considering as significant genetic profiles that, although detected at the crime scene, bear no relevance to the crime itself. The study reported herein was performed to experimentally test the hypothesis that sebaceous fluid may represent an important vector responsible for DNA transfer from skin surface to other surfaces (of objects or other individuals’ bodies) that have been subsequently contacted. Genetic analyses were performed in order to examine primary and secondary DNA transfer and, as to demonstrate the possible origin of “touch DNA”, the presence of fragmented single stranded DNA was investigated by immunohistochemical analysis. Our results show that “touch DNA” secondary transfer is indeed possible from person to person and, in turn, from person to object depending on the specific sebaceous or non sebaceous skin area previously touched. In addition, we demonstrate the presence of fragmented single stranded DNA specifically immunodetected in the vast majority of cells forming the sebaceous gland but not in the epidermis layers, strongly indicating that sebaceous fluid represents an important vector responsible for DNA transfer. In view of our results, forensic investigations need to take into account that the propensity to leave behind genetic material through contact could depend from the individual ability to shed sebaceous fluid on the skin surface. These data shed new light on the field of primary and secondary DNA transfer through contact by showing that propensity to leave behind genetic material depends from the variable activity of sebaceous glands