### ASA 127th Meeting M.I.T. 1994 June 6-10

## 2aNSb5. On the use of artificial neural networks to model sound quality.

**P. Laux
**

**
P. Davies
**

**
**
*Ray W. Herrick Labs., School of Mech. Eng., Purdue Univ., West Lafayette,
IN 47907-1077
*

*
*
Loudness, percentile loudness, sharpness, and fluctuation strength have
been shown in laboratory experiments to be correlated with annoyance. Zwicker
proposed a model of unbiased annoyance (UBA) based on time of day (d),
fluctuation strength (F), sharpness (S), and N[sub 10]. In psychoacoustic tests
on amplitude-modulated noise signals [P. Laux and P. Davies, NCEJ 40 (3)
(May-June 1993)] UBA has been shown to have a higher correlation to annoyance
than other commonly used noise measures, although, other correlation models
were found to produce an even better model of the relationship between (S,N[sub
10],F) and the subjective ratings. The use of artificial neural networks (ANNs)
to describe the relationship between (S,N[sub 10],F) and the subjective
responses has been investigated. The smaller the network, the fewer the number
of weights, and the fewer the number of signals that a subject has to rate. The
objective of the research was to estimate a network that was the smallest
possible model of the data. As a starting point UBA was modeled as a function
of (S,N[sub 10],F); the resulting ANN model of UBA was then used as the initial
conditions when subjective data were available.