Evaluation of an Algorithm for the Automatic Detection of Salient Frequencies in Individual tracks of Multi-track Musical Recording

This paper evaluates the performance of a salient frequency detection algorithm. The algorithm calculates each FFT bin maximum as the maximum value of that bin across an audio region and identifies the FFT bin maximum peaks with the highest five deemed to be the most salient frequencies. To determine the algorithm’s efficacy test subjects were asked to identify the salient frequencies in eighteen audio tracks. These results were compared against the algorithm’s results. The algorithm was successful with electric guitars but struggled with other instruments and in detecting secondary salient frequencies. In a second experiment subjects equalised the same audio tracks using the detected peaks as fixed centre frequencies. Subjects were more satisfied than expected when using these frequencie

An investigation into the efficacy of methods commonly employed by mix engineers to reduce frequency masking in the mixing of multitrack musical recordings

Studio engineers use a variety of techniques to reduce frequency masking between instruments when mixing multi-track musical recordings. This study evaluates the efficacy of three techniques, namely mirrored equalization, frequency spectrum sharing and stereo panning, against their variations to confirm the veracity of accepted practice. Mirrored equalisation involves boosting one instrument and cutting the other at the same frequency. Frequency spectrum sharing involves low pass filtering one instrument and high pass filtering the other. Panning involves placing two competing instruments at different pan positions. Test subjects used eight tools comprising a single unlabeled slider to reduce frequency masking in several two instrument scenarios. Satisfaction values were recorded. Results indicate subjects preferred using tools that panned both audio tracks

A linear noise approximation for stochastic epidemic models fit to partially observed incidence counts

Stochastic epidemic models (SEMs) fit to incidence data are critical to elucidating outbreak dynamics, shaping response strategies, and preparing for future epidemics. SEMs typically represent counts of individuals in discrete infection states using Markov jump processes (MJPs), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic. Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space. Simulation-based computational approaches can address the intractability of the MJP likelihood, but are numerically fragile and prohibitively expensive for complex models. A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings, but requires modification for analyzing incidence counts without assuming that the data are normally distributed. We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally. Our framework is computationally robust when the model dynamics are complex and applies to a broad class of SEMs. We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national surveillance counts from the 2013--2015 West Africa Ebola outbreak

Efficient data augmentation for fitting stochastic epidemic models to prevalence data

Stochastic epidemic models describe the dynamics of an epidemic as a disease spreads through a population. Typically, only a fraction of cases are observed at a set of discrete times. The absence of complete information about the time evolution of an epidemic gives rise to a complicated latent variable problem in which the state space size of the epidemic grows large as the population size increases. This makes analytically integrating over the missing data infeasible for populations of even moderate size. We present a data augmentation Markov chain Monte Carlo (MCMC) framework for Bayesian estimation of stochastic epidemic model parameters, in which measurements are augmented with subject-level disease histories. In our MCMC algorithm, we propose each new subject-level path, conditional on the data, using a time-inhomogeneous continuous-time Markov process with rates determined by the infection histories of other individuals. The method is general, and may be applied, with minimal modifications, to a broad class of stochastic epidemic models. We present our algorithm in the context of multiple stochastic epidemic models in which the data are binomially sampled prevalence counts, and apply our method to data from an outbreak of influenza in a British boarding school

An Investigation into the Relationship Between the Subjective Descriptor Aggressive and the Universal Audio 1176 FET Compressor

In popular music productions, the lead vocal is often the main focus of the mix and engineers will work hard to impart creative colouration on this source. This paper conducts listening experiments to test if there is a correlation between perceived distortion and the descriptor “aggressive” which is often used to describe the sonic signature of the Universal Audio 1176. The results from this study show compression settings that impart audible distortion are perceived as aggressive by the listener and. Furthermore, there is a strong correlation between the subjective scores for distortion and aggressive. eIt was also shown that there is a strong correlation between compression settings measured rated to have 0.5% THD and above were rated as both the most distorted and most aggressive. high aggressive scores and the audio feature roughness

Exploring the Container Metaphor for Equalisation Manipulation

This paper presents the first stage in the design and evaluation of a novel container metaphor interface for equalisation control. The prototype system harnesses the Pepper's Ghost illusion to project mid-air a holographic data visualisation of an audio track's long-term average and real-time frequency content as a deformable shape manipulated directly via hand gestures. The system uses HTML 5, JavaScript and the Web Audio API in conjunction with a Leap Motion controller and bespoke low budget projection system. During subjective evaluation users commented that the novel system was simpler and more intuitive to use than commercially established equalisation interface paradigms and most suited to creative, expressive and explorative equalisation tasks

Audio interfaces should be designed based on data visualisation first principles

Audio mixing interfaces (AMIs) commonly conform to a small number of paradigms. These paradigms have significant shortcomings. Data visualisation first principles should be employed to consider alternatives. Existing AMI paradigms are discussed and concepts of image theory and elementary perceptual elements outlined. AMIs should be evaluated by usability experiments however performing these properly is time-consuming. There are many data visualisation options and combinations. Collaboration with others would enable a greater range to be explored. Better understanding data visualisation will benefit audio and music interface development in general

Novel Designs for the Audio Mixing Interface Based on Data Visualisation First Principles

Given the shortcomings of current audio mixing interfaces (AMIs) this study focuses on the development of alternative AMIs based on data visualisation first principles. The elementary perceptual tasks defined by Cleveland informed the design process. Two design ideas were considered for pan: using the elementary perceptual tasks ‘scale’ to display pan on either a single or multiple horizontal lines. Four design ideas were considered for level: using ‘length’, ‘area’, ‘saturation’ or ‘scalable icon’ for visualisation. Each level idea was prototyped with each pan idea, totalling eight novel interfaces. Seven subjects undertook a usability evaluation, replicating a 16 channel reference mix with each interface. Results showed that ‘scalable icons’, especially on multiple horizontal lines appear to show potential

Formal usability evaluation of audio track widget graphical representation for two-dimensional stage audio mixing interface

The two-dimensional stage paradigm (2DSP) has been suggested as an alternative audio mixing interface (AMI). This study seeks to refine the 2DSP by formally evaluating graphical track visualisation styles. Track visualisations considered were text only, circles containing text, individually coloured circles containing text, circles colour coded by instrument type with text, icons with text superimposed, circles with RMS related dynamic opacity and a traditional AMI. The usability evaluation focused on track selection efficiency and included user visualisation preference for this micro-task. Test subjects were instructed to click five randomly selected tracks for a six, sixteen and thirty-two track mix for each visualisation. The results indicate text only visualisation is best for efficiency however test subjects preferred icons and traditional AMI