AN EVALUATION OF AUDIO FEATURE EXTRACTION TOOLBOXES

Audio feature extraction underpins a massive proportion of audio processing, music information retrieval, audio effect design and audio synthesis. Design, analysis, synthesis and evaluation often rely on audio features, but there are a large and diverse range of feature extraction tools presented to the community. An evaluation of existing audio feature extraction libraries was undertaken. Ten libraries and toolboxes were evaluated with the Cranfield Model for evaluation of information retrieval systems, reviewing the cov-erage, effort, presentation and time lag of a system. Comparisons are undertaken of these tools and example use cases are presented as to when toolboxes are most suitable. This paper allows a soft-ware engineer or researcher to quickly and easily select a suitable audio feature extraction toolbox. 1

Physically derived sound synthesis model of a propeller

© 2017 Copyright held by the owner/author(s). A real-time sound synthesis model for propeller sounds is presented. Equations obtained from fluid dynamics and aerodynamics research are utilised to produce authentic propeller-powered aircraft sounds. The result is a physical model in which the geometries of the objects involved are used in sound synthesis calculations. The model operates in real-time making it ideal for integration within a game or virtual reality environment. Comparison with real propeller-powered aircraft sounds indicates that some aspects of real recordings are not replicated by our model. Listening tests suggest that our model performs as well as another synthesis method but is not as plausible as a real recording

Investigation of Frequency-Specific Loudness Discomfort Levels in Listeners With Migraine: A Case-Control Study

Objectives: Hypersensitivity to auditory stimuli is a commonly reported symptom in listeners with migraine, yet it remains relatively unexplored in research. This study aims to investigate loudness discomfort levels in listeners with migraine, while identifying the frequencies most affected by the phenomenon. Design: To achieve this, the study compared just audible level and loudness discomfort level ranges between participants with and without migraine from the United Kingdom, Greece as well as the participant recruitment platform Prolific, across 13 frequencies from 100 to 12,000 Hz, through an online listening test. Results: Fifty-five participants with migraine and 49 participants without migraine from both countries and Prolific were included in the analysis, where threshold ranges between just audible and mildly uncomfortable levels were compared in 13 frequencies. Migraineur group participants presented significantly smaller ranges between just audible and mildly uncomfortable level, due to lower thresholds of mild discomfort in 12 of the 13 frequencies when compared with the nonmigraineur group participants. Participants taking the test during their migraine attack or aura presented a tendency for smaller ranges. In addition, participants with self-reported higher severity migraine exhibited bigger ranges compared with participants with low severity migraine within the migraineur group. No relationship between ranges and medication or migraine attack frequency within the migraineur group was observed. Conclusions: Results from the study demonstrate a tendency for the migraineur group to present lower thresholds of mild discomfort compared with the nonmigraineur group, aligning with previous studies while extending the phenomenon to more frequencies than those previously examined. Though the present study presented no relationship between ranges and medication or attack frequency, further research is required to investigate a potential link between these factors

Neural Synthesis of Footsteps Sound Effects with Generative Adversarial Networks

Footsteps are among the most ubiquitous sound effects in multimedia applications. There is substantial research into understanding the acoustic features and developing synthesis models for footstep sound effects. In this paper, we present a first attempt at adopting neural synthesis for this task. We implemented two GAN-based architectures and compared the results with real recordings as well as six traditional sound synthesis methods. Our architectures reached realism scores as high as recorded samples, showing encouraging results for the task at hand

User Preference on Artificial Reverberation and Delay Time Parameters

Author P. D. Pestana was sponsored by national funds through the Fundac¸ao para a Ci ˜ encia e a Tecnologia, Portu- ˆ gal, in projects: “PEst-OE/EAT/UI0622/2014” and “PEstOE/MAT/UI2006/2014.” Author J. D. Reiss is supported by EPSRC Platform Grant: Digital Music, EP/K009559