Design and Evaluation of a Probabilistic Music Projection Interface

We describe the design and evaluation of a probabilistic interface for music exploration and casual playlist generation. Predicted subjective features, such as mood and genre, inferred from low-level audio features create a 34- dimensional feature space. We use a nonlinear dimensionality reduction algorithm to create 2D music maps of tracks, and augment these with visualisations of probabilistic mappings of selected features and their uncertainty. We evaluated the system in a longitudinal trial in users’ homes over several weeks. Users said they had fun with the interface and liked the casual nature of the playlist generation. Users preferred to generate playlists from a local neighbourhood of the map, rather than from a trajectory, using neighbourhood selection more than three times more often than path selection. Probabilistic highlighting of subjective features led to more focused exploration in mouse activity logs, and 6 of 8 users said they preferred the probabilistic highlighting mode

Automatic Personalized Playlist Generation

Käesolevas magistritöös on esitatud automaatse personaliseeritud pleilisti tekitaja probleemi lähenemisviiside uuring. Lisaks teoreetilise tausta lühiülevaatele me dokumenteerisime oma lähenemist: meie poolt tehtud katsed ning nende tulemused. Meie algoritm koosneb kahest põhiosast: pleilisti hindamisfunktsiooni konstrueerimine ning pleilisti genereerimisstrateegia valik. Esimese ülesande lahendamiseks on valitud Naive Bayes klassifitseerija ning 5-elemendiline MIRtoolbox tööristakasti poolt kavandatud audio sisupõhiste attribuutide vektor, mis klassiitseerivad pleilisti heaks või halvaks 82% täpsusega - palju parem kui juhuslik klassifitseerija (50%). Teise probleemi lahendamiseks proovisime kolm genereerimisalgoritmi: lohistus (Shuffle), randomiseeritud otsing (Randomized Search) ning geneetiline algoritm (Genetic Algorithm). Vastavalt katsete tulemustele kõige paremini ja kiiremini töötab randomiseeritud otsingu algoritm. Kõik katsed on tehtud 5 ning 10 elemendilistel pleilistidel. Kokkuvõttes, oleme arendanud automatiseeritud personaliseeritud pleilisti tekitaja algoritmi, mis vastavalt meie hinnangutele vastab ka kasutaja ootustele rohkem, kui juhuslikud lohistajad. Algoritmi võib kasutada keerulisema pleilistide konstrueerimiseks

Blending Two Automatic Playlist Generation Algorithms

We blend two existing automatic playlist generation algorithms. One algorithm is built to smoothly transition between a start song and an end song (Start-End). The other infers song similarity based on adjacent occurrences in expertly authored streams (EAS). First, we seek to establish the effectiveness of the Start-End algorithm using the EAS algorithm to determine song similarity, then we propose two playlist generation algorithms of our own: the Unbiased Random Walk (URW) and the Biased Random Walk (BRW). Like the Start-End algorithm, both the URW algorithm and BRW algorithm transition between a start song and an end song; however, issues inherent to the Start-End algorithm lead us to believe that our algorithms may create playlists with smoother transitions between songs

"More of an art than a science": Supporting the creation of playlists and mixes

This paper presents an analysis of how people construct playlists and mixes. Interviews with practitioners and postings made to a web site are analyzed using a grounded theory approach to extract themes and categorizations. The information sought is often encapsulated as music information retrieval tasks, albeit not as the traditional "known item search" paradigm. The collated data is analyzed and trends identified and discussed in relation to music information retrieval algorithms that could help support such activity

Adaptive jukebox : a context-sensitive playlist generator

Nowadays, a lot of users own large collections of music MP3 files. Manually organising such collections into playlists is a tedious task. On the other hand random playlist generation may not always provide the user with an enjoyable experience. Automatic playlist generation is a relatively new field in computer science that address this issue, developing algorithms that can automatically create playlists to suit the user’s preferences. This paper presents our work in this field, where we suggest that playlist generators should be more context-sensitive. We also present Adaptive Jukebox, a context-sensitive, zero-input playlist generator that recommends and plays songs from the user’s personal MP3 collection. Initial experiments suggest that our system is more accurate than both a random generator and a system that does not take context into account.peer-reviewe