Automatic Playlist Generation Research Articles

Dynamic artist-based embeddings with application to playlist generation

Streaming services have become a central platform for music consuming. Often, they rely on large archives on which playlists are created. While many playlists are handcrafted, automatic playlist generation is a rising star, as it lets people explore new music easily. Playlist generation relays on some underlying song organization, which is regularly based on embedding methods. The reliability and flexibility of the constructed embedding reflects on the quality of the generated playlists. One type of embedding is content-based, which organizes the songs using raw audio features. Content-based embeddings typically result in a static representation that captures pairwise song similarity.This paper proposes a content-based embedding process that builds a dynamic, instead of a static embedding that is centered around an artist of interest. This embedding mutually organizes seed artist songs into several clusters as well as the rest of the songs in the dataset. The radio is dynamically generated using the distances in the embedded space, while also taking diversity into account. Moreover, it is scalabile in terms of computational complexity.Empirical results are demonstrated on a public dataset named AcousticBrainz, and evaluated by utilizing the Melon dataset, a novel public dataset for playlists. We show that the proposed dynamic embedding and radio generation algorithm yields a more accurate and diverse sequence when compared to other embedding techniques. The proposed methods may be utilized for other recommendation systems tasks that aim to offer the users a diverse recommendation list based on their initial choice.

Impression and Pleasure-Based Music Playlist Generation Method for Broadening Interest in Music and Shifting Positive Mood

This study proposes an automatic playlist generation method that broadens interest in music and puts listeners in a positive mood. Our previous approach put listeners in a positive mood by listening to a playlist that gradually changes the song’s impression from “Low” to “High.” This study adds a new function to our previous study and broadens listener’s interest in the specific song in a playlist by emphasizing it. Specifically, the proposed method created two points in the playlist to arouse the notice of the specific song and shift listeners into positive moods in the playlist. The rapid song impression-changing point engages a listener’s interest in the song, and the gradual song impression-changing one puts listeners in a positive mood. This study conducted a subjective evaluation of the previous and proposed playlists. There were no significant differences between both methods in shifting listeners into positive moods. However, there were significant differences in attention to the emphasized and other songs. These results indicate that the proposed method may potentially expand interest in music and put listeners in a positive mood.

Music recommendation based on acoustic features from the recording studio

Producers of Electronic Dance Music (EDM) typically spend more time creating, shaping, mixing and mastering sounds, than with aspects of composition and arrangement. They analyze the sound by close listening and by leveraging audio monitoring tools, until they successfully created the desired sound character. DJs of EDM tend to play sets of songs that meet their sound ideal. We use audio monitoring tools from the recording studio to retrieve the sound ideal of the most popular DJs and perform a DJ classification, e.g., to predict “what your favorite DJ would play.” The features include third-octave band VU, RMS and crest factor meters, phase scope, and the channel correlation coefficient. This new set of features and the focus on DJ sets is targeted at EDM as it takes the producer and DJ culture into account. With simple dimensionality reduction and machine learning these recording studio features enable us to attribute a song to a DJ with an accuracy of 63%. The features from the monitoring tools in the recording studio could serve for many applications in music information retrieval, such as genre, style and era classification for music browsing, automatic playlist generation and music recommendation, especially in electronic dance music.

A Hybrid Recommender System for Improving Automatic Playlist Continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, user preferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.

Automated online playlist generation system

This paper presents an online playlist management system designed to provide easy and intuitive management of audio and video playlists. Playlists can be uploaded, combined, downloaded, but also created online and stored for listening. Sharing of playlists is also enabled. State of the art AJAX technologies have been used to developed a fast and intuitive user interface. Automatic online playlist generation based on recognizing local songs and automated Youtube searching and queuing is also part of the system. DOI: http://dx.doi.org/10.11591/ij-ict.v2i3.5667

Automatic playlist generation by applying tabu search

In this paper, we propose a solution to the problem of playlist generation. In order to capture user listening preference and recommend playlists, we maintain user profiles by keeping listening history. Then, we apply the sequential pattern mining algorithm with multiple minimum supports on user profiles to derive constraints. Given a set of derived constraints, we apply the tabu search to generate playlists which match constraints as much as possible. Finally, we implement our prototype and perform experiments to show the feasibility, efficiency, and effectiveness of our approach.

Automatic Playlist Generation from Self-Organizing Music Map

Automatic Playlist Generation from Self-Organizing Music Map

Music Theoretic and Perception-based Features for Audio Key Determination

The musical key of a piece is the fundamental knowledge for many Music Information Retrieval tasks as automatic transcription, chord detection or automatic play list generation. To this end, novel features are proposed and evaluated on the basis of musical knowledge, in this article—a total of 13 feature groups based on chromatic representation include scales, chords, major and minor Probe Tone Ratings, and further derived variations. We present extensive results on a database containing 35 h of originally recorded music covering diverse genres as popular, classical, and Jazz music. In the pre-processing, adjustment of reference pitch classes, dB(A)-weighting, band-pass-filter type selection, frequency selection, and gating time are discussed in detail. In addition to traditional correlation-based approaches we employ a data-driven model using Support Vector Machines. Overall, the proposed features enhance correlation results and their combination boosts data-driven audio key determination accuracy. The latter approach based on learnt models significantly outperforms correlation-based determination by 5.0% and 6.7% absolute for the determination of 12 and 24 major and minor keys (clustered or separately), respectively. Maximum accuracy reaches 77.3% and 62.1% for these 12 or 24 keys over the diverse recordings.

Music playlist generation by adapted simulated annealing

We present the design of an algorithm for use in an interactive music system that automatically generates music playlists that fit the music preferences of a user. To this end, we introduce a formal model, define the problem of automatic playlist generation (APG), and prove its NP-hardness. We use a local search (LS) procedure employing a heuristic improvement to standard simulated annealing (SA) to solve the APG problem. In order to employ this LS procedure, we introduce an optimization variant of the APG problem, which includes the definition of penalty functions and a neighborhood structure. To improve upon the performance of the standard SA algorithm, we incorporated three heuristics referred to as song domain reduction, partial constraint voting, and a two-level neighborhood structure. We evaluate the developed algorithm by comparing it to a previously developed approach based on constraint satisfaction (CS), both in terms of run time performance and quality of the solutions. For the latter we not only considered the penalty of the resulting solutions, but we also performed a conclusive user evaluation to assess the subjective quality of the playlists generated by both algorithms. In all tests, the LS algorithm was shown to be a dramatic improvement over the CS algorithm.

Automatic playlist generation based on tracking user’s listening habits

Algorithms for automatic playlist generation solve the problem of tedious and time consuming manual selection of musical playlists. These algorithms generate playlists according to the user's music...