This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduces Kernel Meta-Training, which is a method of learning a Gaussian Process kernel from a distribution o...

This paper proposes content-based music information retrieval (MIR) methods based on user preferences, which aim to improve the accuracy of MIR for users with “diverse” preferences, i.e., users whose preferences range in songs with a wide variety of features. The proposed MIR method dynamically generates an optimal set of query vectors from the sample set of songs submitted by the user to expre...

In most popular music, the vocals sung by the lead singer are the focal point of the song. The unique qualities of a singer’s voice make it relatively easy for us to identify a song as belonging to that particular artist. With little training, if one i s familiar with a particular singer’s voice one can usually recognize that voice in other pieces, even when hearing a song for the first time. T...

The term ‘sampling’ refers to the usage of snippets or loops from existing songs or sample libraries in new songs, mashups, or other music productions. The ability to automatically detect sampling in music is, for instance, beneficial for studies tracking artist influences geographically and temporally. We present a method based on Non-negative Matrix Factorization (NMF) and Dynamic Time Warpin...

The recent explosion of online streaming services is a reflection of the high importance the multimedia has in our everyday life. These systems deal with large collections of media pieces and their usability is tightly related to the meta-data associated with content. As long as the meta-data is correctly assigned users can easily reach what they were looking for. The presence of poorly annotat...

In the era of disruption, digitalization has replaced traditional culture due to technological developments. One form is distributing music or songs which was all on cassettes CDs (compact discs) digital platforms. Currently, there are many platforms such as Apple Music. problems outlined in this paper regarding moral rights songwriters Music, while inherent Creator. This aims to: (1) find out ...

We propose an attribute-based taxonomy approach to providing alternative labels to music. Labels, such as genre, are often used as ground-truth for describing song similarity in music information retrieval (MIR) systems. A consistent labelling scheme is usually a key in determining quality of classifier learning in training and performance in testing of an MIR system. We examine links between c...

Navigating increasingly large personal music libraries is commonplace. Yet most music browsers do not enable their users to explore their collections in a guided and manipulable fashion, often requiring them to have a specific target in mind. MusicBox is a new music browser that provides this interactive control by mapping a music collection into a two-dimensional space, applying principal comp...

Abstract—Music brings people together, it allows us to experience the same emotions. Currently musical genre classification is done manually and requires even the trained human ear considerable effort. Therefore, clustering songs automatically and then drawing valuable insights from those clusters is an interesting problem and can add great value to music information retrieval systems. Most of ...

One of the biggest challenges facing current methods for classifying music by genre or artist is that features of the sound are computed on very small temporal scales (20 to 50 milliseconds), while the labels need to be assigned at relatively large temporal scales (3 to 5 minutes). We address this challenge by partitioning songs into smaller pieces and classifying each one separately. Our choic...