Placeholder image for staff profiles

Mr Andy Pearce


Research Fellow in Timbral Modelling

My publications

Publications

Pearce A, Brookes TS, Mason R, Dewhirst M (2016) Measurements to determine the ranking accuracy of perceptual models, Audio Engineering Society
Linear regression is commonly used in the audio industry to create objective measurement models that predict
subjective data. For any model development, the measure used to evaluate the accuracy of the prediction is
important. The most common measures assume a linear relationship between the subjective data and the prediction,
though in the early stages of model development this is not always the case. Measures based on rank ordering (such
as Spearman?s test), can alternatively be used. Spearman?s test, however, does not consider the variance of the
subjective data. This paper presents a method of incorporating the subjective variance into the Spearman?s rank
ordering test using Monte Carlo simulations, and shows how this can be beneficial in the development of predictive
models.
Pearce A, Brookes TS, Dewhirst M (2015) Validation of Experimental Methods to Record Stimuli for Microphone Comparisons, 139th International AES Convention papers Audio Engineering Society
Test recordings can facilitate evaluation of a microphone's characteristics but there is currently no standard or experimentally validated method for making recordings to compare the perceptual characteristics of microphones. This paper evaluates previously used recording methods, concluding that, of these, the most appropriate approach is to record multiple microphones simultaneously. However, perceived differences between recordings made with microphones in a multi-microphone array might be due to (i) the characteristics of the microphones and/or (ii) the different locations of the microphones. Listening tests determined the maximum acceptable size of a multi-microphone array to be 150 mm in diameter, but the diameter must be reduced to no more than 100 mm if the microphones to be compared are perceptually very similar.
Pearce A, Brookes TS, Mason RD (2017) Timbral attributes for sound effect library searching, AES E-Library Audio Engineering Society
To improve the search functionality of online sound effect libraries, timbral information could be extracted using
perceptual models, and added as metadata, allowing users to filter results by timbral characteristics. This paper
identifies the timbral attributes that end-users commonly search for, to indicate the attributes that might usefully be
modelled for automatic metadata generation. A literature review revealed 1187 descriptors that were subsequently
reduced to a hierarchy of 145 timbral attributes. This hierarchy covered the timbral characteristics of source types
and modifiers including musical instruments, speech, environmental sounds, and sound recording and reproduction
systems. A part-manual, part-automated comparison between the hierarchy and a freesound.org search history
indicated that the timbral attributes hardness, depth, and brightness occur in searches most frequently.
Safavi Saeid, Pearce Andy, Wang Wenwu, Plumbley Mark (2018) Predicting the perceived level of reverberation using machine learning, Proceedings of the 52nd Asilomar Conference on Signals, Systems and Computers (ACSSC 2018) Institute of Electrical and Electronics Engineers (IEEE)
Perceptual measures are usually considered more
reliable than instrumental measures for evaluating the perceived
level of reverberation. However, such measures are time consuming
and expensive, and, due to variations in stimuli or assessors,
the resulting data is not always statistically significant. Therefore,
an (objective) measure of the perceived level of reverberation
becomes desirable. In this paper, we develop a new method to
predict the level of reverberation from audio signals by relating
the perceptual listening test results with those obtained from a
machine learned model. More specifically, we compare the use of
a multiple stimuli test for within and between class architectures
to evaluate the perceived level of reverberation. An expert set
of 16 human listeners rated the perceived level of reverberation
for a same set of files from different audio source types. We
then train a machine learning model using the training data
gathered for the same set of files and a variety of reverberation
related features extracted from the data such as reverberation
time, and direct to reverberation ratio. The results suggest that
the machine learned model offers an accurate prediction of the
perceptual scores.