Content-based music audio recommendation

Abstract

We present the MusicSurfer, a metadata free system for the interaction with massive collections of music. MusicSurfer automatically extracts descriptions related to instrumentation, rhythm and harmony from music audio signals. Together with efficient similarity metrics, the descriptions allow navigation of multimillion track music collections in a flexible and efficient way without the need for metadata nor human ratings.

A Music Information Retrieval Approach Based on Power Laws

“…Although there may exist other possibilities for a piece's preference among CMA listeners (e.g., how famous it is), given the size of the corpus and the large 1 A pilot study appears in [9]. separation between the two classes, we believe that these possibilities are for the most part subsumed by aesthetic preference.…”

“…The field of music information retrieval (MIR) focuses on retrieving information from large, on-line repositories of music content, using various forms of query-based or navigation-based approaches [1,2,3,4]. MIR techniques can be applied in a wide variety of contexts, ranging from searches in music libraries (e.g., [5]), to consumeroriented music e-commerce environments [6].…”

Taxonomy-Oriented Recommendation towards Recommendation with Stage

“…The effectiveness of content-based information filtering paradigm has been proven for applications locating textual documents relevant to a topic. Particularly in recommender systems, contentbased methods [1,4,7] enable accurate comparison between different textual or structural items, and hence recommend items similar to a user's consumption history. However, content-based filtering approaches suffer from multiple drawbacks, e.g., strong dependence on the availability of content, ignoring the contextual information of recommendation, and etc.…”

Using rich social media information for music recommendation via hypergraph model

“…For the task of music recommendation, the most common approach is to directly analyze the audio signal. These methods are called acoustic-based music recommendation [Logan 2004;Cano et al 2005;Cai et al 2007;Rho et al 2009]. Due to the semantic gap between low level acoustic features and high level music concepts [Celma 2006], the results of acousticbased music recommendation are not satisfactory.…”