D istinctive stimuli are better recognized than typical stimuli in many domains (e.g., faces, words). Distinctiveness predicts the point of recognition of a melody ( Bailes, 2010 ), and the recognition of unique tones within a melody ( Vuvan, Podolak, & Schmuckler, 2014 ), yet no studies have examined the role of distinctiveness in recognizing whole melodies. We composed a set of novel melodies according to rules that should result in these being perceived as more or less distinctive. Using computational analysis and human ratings by a group of 36 pilot testers, we established a final stimulus set of 96 novel melodies (48 eightnote, 48 sixteen-note), half of which were high and half low in distinctiveness. A separate group of 26 participants completed a recognition test using this stimulus set. Using linear mixed-effects modeling, we found that greater pitch and interval range, wider intervals, varied contour, and ambiguous tonality within a Western diatonic framework predicted human perception of distinctiveness. However, only a wider modal (most frequent) interval predicted correct recognition. Distinctiveness improved recognition performance in both stimulus lengths; however, a significant advantage was only shown for sixteen-note melodies. Thus, the distinctiveness effect as observed across domains generalizes to the recognition of longer, whole melodies.

We investigated how well structural features such as note density or the relative number of changes in the melodic contour could predict success in implicit and explicit memory for unfamiliar melodies. We also analyzed which features are more likely to elicit increasingly confident judgments of “old” in a recognition memory task. An automated analysis program computed structural aspects of melodies, both independent of any context, and also with reference to the other melodies in the testset and the parent corpus of pop music. A few features predicted success in both memory tasks, which points to a shared memory component. However, motivic complexity compared to a large corpus of pop music had different effects on explicit and implicit memory. We also found that just a few features are associated with different rates of “old” judgments, whether the items were old or new. Rarer motives relative to the testset predicted hits and rarer motives relative to the corpus predicted false alarms. This data-driven analysis provides further support for both shared and separable mechanisms in implicit and explicit memory retrieval, as well as the role of distinctiveness in true and false judgments of familiarity.

LISTENERS ARE THOUGHT TO BE CAPABLE of perceiving multiple voices in music. This paper presents different views of what 'voice' means and how the problem of voice separation can be systematically described, with a view to understanding the problem better and developing a systematic description of the cognitive task of segregating voices in music. Well-established perceptual principles of auditory streaming are examined and then tailored to the more specific problem of voice separation in timbrally undifferentiated music. Adopting a perceptual view of musical voice, a computational prototype is developed that splits a musical score (symbolic musical data) into different voices. A single 'voice' may consist of one or more synchronous notes that are perceived as belonging to the same auditory stream. The proposed model is tested against a small dataset that acts as ground truth. The results support the theoretical viewpoint adopted in the paper.