Abstract
Being an important auditory attribute of sound, timbre exhibits great potential for classifying sound source and its suitable representation and parameterization are crucial for feature extraction. In this study, we express environmental sound's timbre in terms of verbal description and its projection in independent space, which are respectively referred to as Natural Timbre (NT) and Essential Timbre (ET). In this study, such two kinds of timbre expressions are applied to acoustic target recognition using synthesized steady-state underwater noise with two subjective rating experiments. First a semantic differential test is conducted and the NT-based target identification rates are achieved by forced clustering; then a paired comparison experiment is carried out to get the ET-based identification rates. Finally, the recognition performances for two kinds of timbre representations are compared and its advantages are discussed in association with feature extraction and acoustic target recognition.
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