An adaptive distance measure for similarity based playlist generation

被引：0

作者：

Gaertner, D. ^{[1
]}

Kraft, F. ^{[1
]}

Schaaf, T. ^{[2
]}

机构：

[1] Univ Karlsruhe, InterACT, Fasanengarten 1, Karlsruhe, Germany

[2] Carnegie Mellon Univ, InterACT, Pittsburgh, PA USA

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL I, PTS 1-3, PROCEEDINGS | 2007年

关键词：

playlist generation; user adaptation; skipping behavior; music similarity; music information retrieval;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Nowadays, a large part of all music ever recorded is digitally available and due to MP3 already ten thousands of songs can be carried around on a mobile device. Intelligent automatic song selection is more and more required alternatively to random selection or manual playlist generation. We propose a system, that generates playlists including songs similar to accepted ones, discarding songs similar to rejected ones, where similar refers to timbre. Additional adaptivity is achieved with a user-adaptive distance function which in our case requires modeling features separately. After a seed-song (which is the first accepted song) is given by the user, the distance function is used by a song selection strategy to select songs. Minimal user feedback is collected with a skip button that is pressed to directly jump to the next song and explicitly reject the current one while acceptance is implicitly given by listening to a song.

引用

页码：229 / +

页数：2