Play it again: did this melody occur more frequently or was it heard more recently? The role of stimulus familiarity in episodic recognition of music

Episodic recognition of novel and familiar melodies was examined by asking participants to make judgments about the recency and frequency of presentation of melodies over the course of two days of testing. For novel melodies, recency judgments were poor and participants often confused the number of presentations of a melody with its day of presentation; melodies heard frequently were judged as have been heard more recently than they actually were. For familiar melodies, recency judgments were much more accurate and the number of presentations of a melody helped rather than hindered performance. Frequency judgments were generally more accurate than recency judgments and did not demonstrate the same interaction with musical familiarity. Overall, these findings suggest that (1) episodic recognition of novel melodies is based more on a generalized feeling of familiarity than on a specific episodic memory, (2) frequency information contributes more strongly to this generalized memory than recency information, and (3) the formation of an episodic memory for a melody depends either on the overall familiarity of the stimulus or the availability of a verbal label. (C) 2004 Elsevier B.V. All rights reserved.

Exploring composite acoustic features for efficient music similarity query

Music similarity query based on acoustic content is becoming important with the ever-increasing growth of the music information from emerging applications such as digital libraries and WWW. However, relative techniques are still in their infancy and much less than satisfactory. In this paper, we present a novel index structure, called Composite Feature tree, CF-tree, to facilitate efficient content-based music search adopting multiple musical features. Before constructing the tree structure, we use PCA to transform the extracted features into a new space sorted by the importance of acoustic features. The CF-tree is a balanced multi-way tree structure where each level represents the data space at different dimensionalities. The PCA transformed data and reduced dimensions in the upper levels can alleviate suffering from dimensionality curse. To accurately mimic human perception, an extension, named CF+-tree, is proposed, which further applies multivariable regression to determine the weight of each individual feature. We conduct extensive experiments to evaluate the proposed structures against state-of-art techniques. The experimental results demonstrate superiority of our technique.