Automatically generated playlists have become an important medium for accessing and exploring large collections of music. In this paper, we present a probabilistic model for generating coherent playlists by embedding songs and social tags in a unified metric space. We show how the embedding can be learned from example playlists, providing the metric space with a probabilistic meaning for song/s...

Online music services are increasing in popularity. They enable us to analyze people’s music listening behavior based on play logs. Although it is known that people listen to music based on topic (e.g., rock or jazz), we assume that when a user is addicted to an artist, s/he chooses the artist’s songs regardless of topic. Based on this assumption, in this paper, we propose a probabilistic model...

An interesting problem in music information retrieval is how to combine the information from different sources in order to improve retrieval effectiveness. This paper introduces an approach to represent a collection of tagged songs through an hidden Markov model with the purpose to develop a system that merges in the same framework both acoustic similarity and semantic descriptions. The former ...

In this paper, we propose a new approach to query-by-humming, focusing on MP3 songs database. Since MP3 songs are much more difficult in melody representation than symbolic performance data, we adopt to extract feature descriptors from the vocal sounds part of the songs. Our approach is based on signal filtering, sub-band spectral processing, MDCT coefficients analysis and peak energy detection...

Many interesting pieces of music violate established structures or rules of their genre on purpose. These songs can be very atypical in their interior structure and their different parts might actually allude to entirely different other songs or genres. We present a query-by-example-based user interface that shows songs related to the one currently playing. This relation is not based on overall...

This project relies on computer vision techniques to build a practical music retrieval system. Our approach tackles traditional music identification as a corrupted sub-image retrieval problem from the 2-D spectrogram representation of the original songs. More specifically, a query snippet spectrogram is matched against our database using its descriptor representation. We utilize a novel pairwis...

This paper focuses on cover song identification among datasets potentially containing millions of songs. A compact representation of music contents plays an important role in large-scale analysis and retrieval. The proposed approach is based on high-level summarization of musical songs using chord profiles. Search is performed in two steps. In the first step, the Locality Sensitive Hashing (LHS...

Automatic playlist generation can be a useful tool to navigate the myriad choices available to users in music services today. Here, we present our recent work on explicitly modeling playlists without requiring external similarity measures. Our Logistic Markov Embedding is trained directly on historical playlist data and can unify songs and (when available) social tags in a Euclidean space. The ...

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [20]. In the music domain recommender systems can support information search and discovery tasks by helping the user to find relevant music items, for instance, new music tracks, or artists that the user may not even know [18, 9]. Several techniques have been proposed but most of t...

A crucial dimension of Content-based music management systems is their ability to compute automatically similarities between music titles. We propose a technique that allows users to find music titles that sound similar to songs they like. The technique relies on a modelling of the timbral characteristics of a music signal by distributions of Cepstrum coefficients. The resulting models are then...