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Networks are fast replacing point-to-point analog and digital connections, both as a means of transferring audio between devices and as a vehicle for control or discovery. The Audio-over-IP Summit, held during the Fall 2020 Convention, included a number of sessions during which industry experts offered guidance through what may seem like a jungle of standards, proprietary solutions, and alliances.
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While room acoustic measurements can accurately capture the sound field of real rooms, they are usually time consuming and tedious if many positions need to be measured. Therefore this contribution presents the Autonomous Robot Twin System for Room Acoustic Measurements (ARTSRAM) to autonomously capture large sets of room impulse responses with variable sound source and receiver positions. The proposed implementation of the system consists of two robots, one of which is equipped with a loudspeaker, while the other one is equipped with a microphone array. Each robot contains collision sensors, thus enabling it to move autonomously within the room. The robots move according to a random walk procedure to ensure a big variability between measured positions. A tracking system provides position data matching the respective measurements. After outlining the robot system, this paper presents a validation, in which anechoic responses of the robots are presented and the movement paths resulting from the random walk procedure are investigated. Additionally the quality of the obtained room impulse responses is demonstrated with a sound field visualization. In summary, the evaluation of the robot system indicates that large sets of diverse and high-quality room impulse responses can be captured with the system in an automated way. Such large sets of measurements will benefit research in the fields of room acoustics and acoustic virtual reality.
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Drawing upon the survey instruments of Lewis and Neville [1], Nadal [2], and Yang and Carroll [3], we conducted an online survey that captured experiences of discrimination and microaggressions reported by 387 recording engineers, producers, and studio assistants living in 46 different countries. Our statistical analyses reveal highly significant and systemic gender inequalities within the field, e.g., cisgender women experience many more sexually inappropriate comments (p < e-14, large effect size) and unwanted comments about their physical appearance (p < e-12, large effect size) than cisgender men, and they are much more likely to face challenges to their authority (p < e-13, large effect size) and expertise (p < e-10, large effect size). A comparison of our results with a study about women’s experiences of microaggressions within STEM academia [3] indicates that the recording studio workplace scores 33% worse on the silencing and marginalization of women, 33% worse on gender-related workplace microaggressions, and 24% worse on sexual objectification. These findings call for serious reflection on the part of the community to progress from awareness to collective action that will unlock the control room for women and other historically and systemically marginalized groups of studio professionals.
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Modern feature-based methodologies in semantic audio applications attempt to capture the temporal dependency of successive feature observations, which form the so-called texture windows. This paper proposes an enhancement of this type of processing, known as temporal feature integration, by employing and testing alternative deployable strategies. Specifically, data are fitted through commonly used statistical principles, estimating the parameters of a given probability density function that maximize the log-likelihood of the samples inside each texture window. The main statistical model that is set under investigation is the alpha-stable distribution because it can successfully represent signals, which the commonly used Gaussian curves fail to capture. Within this framework, the enhanced feature integration method is also elaborated, introducing new measures for feature modeling. The main objective of this work is to introduce an efficient feature engineering protocol for temporal integration, specifying a compact and robust set of aggregated audio parameters that can address the needs of many audio information retrieval systems.
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