A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Lymphoepithelioma-like carcinoma of the vulva, an underrecognized entity? Case report with a single inguinal micrometastasis detected by sentinel node technique

This report describes an unusual EBV-negative lymphoepithelioma-like carcinoma of the vulva in a 73-year-old patient. The lesion was localised at the right minor labium and was resected by partial vulvectomy. A synchronous sentinel lymph node biopsy revealed a single micrometastasis in the right inguinal region, which prompted local radiotherapy. Follow-up nine months later showed only slight vulvar atrophy, without signs of local recurrence or distant metastases

Classification of audio sources using ad-hoc microphone arrays

Die automatische Klassifikation von akustischen Quellen ist Bestandteil vieler Algorithmen der Audiosignalverarbeitung, z.B. bei der Reduktion von Störanteilen in einem Signal oder der Analyse akustischer Szenen. Mögliche Anwendungen finden sich u. a. in der Telekommunikation und in der automatischen Spracherkennung. Die Klassifikation ist insb. dann eine anspruchsvolle Aufgabe, wenn Signale, die mit einem Mikrofon empfangen werden, durch räumlich bedingten Nachhall beeinflusst werden und wenn mehrere Quellen in einem Raum simultan aktiv sind. In der vorliegenden Arbeit wird ein System vorgestellt, das die Signale eines Ad-hoc Mikrofon-Arrays verarbeitet um Schallquellen in einer halligen Umgebung zu klassifizieren. Werden automatisch ermittelte Mikrofon-Cluster und die entwickelten Strategien zur Kombination der Informationen zwischen den Clustern genutzt, lassen sich akustische Quellen mit einer höheren Genauigkeit als unter der Verwendung einzelner Mikrofonsignale klassifizierenThe automatic classification of audio sources is an important ingredient in many audio signal processing algorithms, e.g., for noise reduction or acoustic scene analysis. Possible applications are, e.g., in the field of telecommunication or automatic speech recognition. In a real world scenario however, the classification constitutes a difficult problem. Often, reverberation and interfering sounds reduce the quality of a target source signal. To classify disturbed signals more accurately the spatial distribution of microphones from ad-hoc microphone arrays can be exploited. In this work we present a system which processes signals from ad-hoc distributed microphones in order to classify multiple simultaneously active and spatially distributed sources. It is shown that the classification accuracy is higher when clusters of microphones are estimated and audio feature information is exchanged within and in between the clusters compared to the classification based on single microphones

A hierarchical approach for the online, on-board detection and localisation of brake squeal using microphone arrays

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