Towards evaluation of example-based audio retrieval system using affective dimensions

Real world audio clips contain numerous acoustic sources. The rich acoustic information they carry cannot be fully described with single or even multiple terms about the acoustic sources alone. For instance, the label birds assigned to a birds singing clip that includes sounds of trees and a small river does not properly capture the experience it creates in the person listening to it. In this paper we introduce a novel scheme where the subjective experience of listening to sound clips containing mixture of sources is captured using affective measures. Furthermore, in contrast to the conventional approach of simple label-based methods, the affective ratings are then used to evaluate the performance of an example-based audio retrieval system. We argue that audio retrieval systems can benefit from using affective measures which are well established in experimental psychology, especially when dealing with real world audio clips. We present experimental results of our pilot study to support this motivation where the latent indexing framework has been employed for example-based retrieval on a collection of clips from the BBC sound effects library. The result of our study indicates that using the scheme of affective measures for representation and evaluation is indeed a promising direction to explore.