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Assessment of measurement-based methods for separating wheel and track contributions to railway rolling noiseApplied Acoustics, https://doi.org/10. 1016/j.apacoust.2018.05.012 Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. AbstractThe noise produced during a train pass-by originates from several different sources such as propulsion noise, noise from auxiliary equipment, aerodynamic noise and rolling noise. The rolling noise is radiated by the wheels and the track and is excited by the wheel and rail unevenness, usually referred to as roughness. The current TSI Noise certification method, which must be satisfied by all new mainline trains in Europe, relies on the use of a reference track to quantify the noise from new vehicles. The reference track is defined by an upper limit of the rail roughness and a lower limit of the track decay rate (TDR). However, since neither the rail roughness nor the track radiation can be completely neglected, the result cannot be taken as representing only the vehicle noise and the measurement does not allow separate identification of the noise radiated by wheel and track. It is even likely that further reductions in the limit values for new rolling stock cannot be achieved on current tracks.There is therefore a need for a method to separate the noise into these two components reliably and cheaply. The purpose of the current study is to assess existing and new methods for rolling noise separation. Field tests have been carried out under controlled conditions, allowing the different methods to be compared. The TWINS model is used with measured vibration data to give reference estimates of the wheel and track noise components. Six different methods are then considered that can be used to estimate the track component. It is found that most of these methods can obtain the track component of noise with acceptable accuracy. However, apart from the TWINS model, the wheel noise component could only be estimated directly using three methods and unfortunately these did not give satisfactory results in the current tests.

Single layer planar near-field acoustic holography for compact sources and a parallel reflector. Journal of Sound and Vibration AbstractWe consider the problem of planar near-field acoustic holography (PNAH) and introduce a new reconstruction method that can be used to process single layer pressure measurements performed in the presence of a reflective surface that is parallel to the measurement plane. The method is specially tailored for compact sources, or for problems in which the scattered field due to the source can be neglected. The approach consists in formulating a seismic model (WRW model) in wavenumber-space and employ it for sound source reconstructions. The proposed method is validated with numerical and experimental data, and, although the most accurate results are obtained when an estimate of the surface impedance is known beforehand, we show that it can substantially improve the reconstruction performance with respect to that of free-field PNAH.

In order to counteract the problem of railway noise and its environmental impact, passing trains in Europe must be tested in accordance to a noise legislation that demands the quantification of the noise generated by the vehicle alone. However, for frequencies between about 500 Hz and 1600 Hz, it has been found that a significant part of the measured noise is generated by the rail, which behaves like a distributed source and radiates plane waves as a result of the contact with the train's wheels. Thus the need arises for separating the rail contribution to the pass-by noise in that particular frequency range. To this end, the present paper introduces a wavenumber-domain filtering technique, referred to as wave signature extraction, which requires a line microphone array parallel to the rail, and two accelerometers on the rail in the vertical and lateral direction. The novel contributions of this research are: (i) the introduction and application of wavenumber (or plane-wave) filters to pass-by data measured with a microphone array located in the near-field of the rail, and (ii) the design of such filters without prior information of the structural properties of the rail. The latter is achieved by recording the array pressure, as well as the rail vibrations with the accelerometers, before and after the train pass-by. The performance of the proposed method is investigated with a set of pass-by measurements performed in Germany. The results seem to be promising when compared to reference data from TWINS, and the largest discrepancies occur above 1600 Hz and are attributed to plane waves radiated by the rail that so far have not been accounted for in the design of the filters.

The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.