Stagger Lee : how violent nostalgia created an American folk song standard

“Stagger” Lee Shelton (1865-1912) was an African-American carriage driver and sometime-pimp from Missouri. He became immortalized in song as a folklore antihero after murdering a drinking partner following a political argument gone bad in a St Louis saloon on Christmas day, 1895. Sentenced to 25 years in prison, Shelton died in Missouri State Penitentiary after violating his parole with a subsequent conviction for assault and robbery. The song, Stack-a-Lee was first documented in 1897, becoming well known in African American communities along the lower Mississippi River over the following decade as Stagolee, Stagger Lee, Stack OLee and other variants. Two versions were published in the Journal of American Folklore in 1911, with notable recordings entering the charts in the 1920s and beyond. Stagger Lee embodies the archetype of a violent and dangerous antihero as his story is retold, and reimagined or referenced in film, becoming a potent symbol of racial conflict in the United States.In both music and cinematic reincarnations, Stagger Lee seems to have an enduring popularity, partly due to the changing nature of his story, which ensures his tale remains up-to-date (it was most recently adapted to a musical in 2015). This article considers how and why this paean to violence, with its fetishistic vision of extreme masculinity, has become something of a standard in the American folk canon

What our bodies tell us about noise

Noise is defined as unwanted sound. Not only is noise unwanted, it is expensive and bad for human health. But what sounds like noise to one person might be a happy sound for someone else, so how do we study noise? This article explains how we try to understand and measure noise. We run experiments in a laboratory to measure how noises in the environment affect listeners’ thinking, stress levels, and health. We measure the listeners’ brain activity, how much they sweat, and their heartbeat changes in response to noises like car engines, train squeals, and airplanes taking off. We match up the brain activity we measure with what people tell us about their responses to noisy sounds. This work will help us to make the world sound better for everyone—the more we understand how we hear, and design better places and spaces to improve our experiences with sound

On the use of AI for generation of functional music to improve mental health

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reduction of cases of dementia in elderly populations, and improvements in markers of general mental well-being such as stress reduction. Here, we describe short case studies addressing general mental well-being (anxiety, stress-reduction) through AI-driven music generation. Engaging in active listening and music-making activities (especially for at risk age groups) can be particularly beneficial, and the practice of music therapy has been shown to be helpful in a range of use cases across a wide age range. However, access to music-making can be prohibitive in terms of access to expertise, materials, and cost. Furthermore the use of existing music for functional outcomes (such as targeted improvement in physical and mental health markers suggested above) can be hindered by issues of repetition and subsequent over-familiarity with existing material. In this paper, we describe machine learning (ML) approaches which create functional music informed by biophysiological measurement across two case studies, with target emotional states at opposing ends of a Cartesian affective space (a dimensional emotion space with points ranging from descriptors from relaxation, to fear). We use Galvanic skin response (GSR) as a marker of psychological arousal and as an estimate of emotional state to be used as a control signal in the training of the ML algorithm. This algorithm creates a non-linear time series of musical features for sound synthesis ‘on-the-fly’, using a perceptually informed musical feature similarity model. We find an interaction between familiarity (or more generally, the featureset model we have implemented) and perceived emotional response so focus on generating new, emotionally-congruent pieces. We also report on subsequent psychometric evaluation of the generated material, and consider how these - and similar techniques -might be useful for a range of functional music generation tasks, for example in nonlinear sound-tracking such as that found in interactive media or video games

Sonic enhancement of virtual exhibits

Museums have widely embraced virtual exhibits. However, relatively little attention is paid to how sound may create a more engaging experience for audiences. To begin addressing this lacuna, we conducted an online experiment to explore how sound influences the interest level, emotional response, and engagement of individuals who view objects within a virtual exhibit. As part of this experiment, we designed a set of different soundscapes, which we presented to participants who viewed museum objects virtually. We then asked participants to report their felt affect and level of engagement with the exhibits. Our results show that soundscapes customized to exhibited objects significantly enhance audience engagement. We also found that more engaged audience members were more likely to want to learn additional information about the object(s) they viewed and to continue viewing these objects for longer periods of time. Taken together, our findings suggest that virtual museum exhibits can improve visitor engagement through forms of customized soundscape design

Generation and analysis of artificial warning sounds for electric scooters

•emTransit B.V (Dott) is a European mobility operator currently operating over 30,000 electric scooters in Belgium, France, Germany, Italy, Poland and now the UK. The company aims to expand its UK operations and has recently won a tender for the Transport for London e-scooter trials.•Dott scooters is looking to mitigate potential safety hazards to pedestrians with the use of an Acoustics Vehicle Alerting System (AVAS) for distinct e-scooter category. •This report presents the work carried out by Salford’s Acoustics Research Centre (ARC) to create a stand-alone device to generate warning sounds for Dott’s e-scooters.•This report includes:oSound generation processoAnalysis of the warning soundsoExplanation of the implementation of a subjective experimentoConclusions and recommendations for next steps, including how to optimise the sound generation system on the scooter, and how to continue the research for designing optimal warning sounds to maximise vehicle noticeability without increasing noise annoyance.•Key outputs are:oA system (including hardware and software) has been developed to generate in real time a warning sound, according to the scooter’s operating conditions (e.g., vehicle speed).oA laboratory study has been carried out to gauge pedestrian awareness of an approaching e-scooter with and without a warning sound added. Preliminary results suggest that a significant benefit, in terms of vehicle noticeability, is observed with the addition of a warning sound. Of the sounds tested, the addition of a broadband sound with modulated tones seems to be the most effective sound increasing vehicle noticeability.•The development of technologies, innovations, goods and services within the Clean Growth sector, for instance sustainable and inclusive micro-mobility, is in strategic alignment with the University of Salford

The Scientific Measurement System of the Gravity Recovery and Interior Laboratory (GRAIL) Mission

The Gravity Recovery and Interior Laboratory (GRAIL) mission to the Moon utilized an integrated scientific measurement system comprised of flight, ground, mission, and data system elements in order to meet the end-to-end performance required to achieve its scientific objectives. Modeling and simulation efforts were carried out early in the mission that influenced and optimized the design, implementation, and testing of these elements. Because the two prime scientific observables, range between the two spacecraft and range rates between each spacecraft and ground stations, can be affected by the performance of any element of the mission, we treated every element as part of an extended science instrument, a science system. All simulations and modeling took into account the design and configuration of each element to compute the expected performance and error budgets. In the process, scientific requirements were converted to engineering specifications that became the primary drivers for development and testing. Extensive simulations demonstrated that the scientific objectives could in most cases be met with significant margin. Errors are grouped into dynamic or kinematic sources and the largest source of non-gravitational error comes from spacecraft thermal radiation. With all error models included, the baseline solution shows that estimation of the lunar gravity field is robust against both dynamic and kinematic errors and a nominal field of degree 300 or better could be achieved according to the scaled Kaula rule for the Moon. The core signature is more sensitive to modeling errors and can be recovered with a small margin