Random Playlists Smoothly Commuting Between Styles

01 Random Playlists Smoothly Commuting Between Styles MARCOS ALVES DE ALMEIDA, Universidade Federal de Minas Gerais, Brazil CAROLINA COIMBRA VIEIRA, Universidade Federal de Minas Gerais, Brazil PEDRO OLMO STANCIOLI VAZ DE MELO, Universidade Federal de Minas Gerais, Brazil RENATO MARTINS ASSUNÇÃO, Universidade Federal de Minas Gerais, Brazil Someone enjoys listening to playlists while commuting. He wants a different playlist of n songs each day, but always starting from Locked Out of Heaven, a Bruno Mars song. The list should progress in smooth transitions between successive and randomly selected songs until it ends up at Stairway to Heaven, a Led Zepellin song. The challenge of automatically generating random and heterogeneous playlists is to find the appropriate balance among several conflicting goals. We propose two methods for solving this problem. One is called ROPE, and it depends on a representation of the songs in an Euclidean space. It generates a random path through a Brownian Bridge that connects any two songs selected by the user in this music space. The second is STRAW , which constructs a graph representation of the music space where the nodes are songs and edges connect similar songs. STRAW creates a playlist by traversing the graph through a steering random walk that starts on a selected song and is directed towards a target song also selected by the user. When compared with the state of the art algorithms, our algorithms are the only ones that satisfy the following quality constraints: heterogeneity, smooth transitions, novelty, scalability, and usability. We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs and make available a prototype. CCS Concepts: • Information systems → Music retrieval; • Applied computing → Sound and music computing; Additional Key Words and Phrases: Music, Sound and Music Computing, System applications and experience, Knowledge and data engineering tools and techniques. ACM Reference Format: Marcos Alves de Almeida, Carolina Coimbra Vieira, Pedro Olmo Stancioli Vaz De Melo, and Renato Martins Assunção. 2019. Random Playlists Smoothly Commuting Between Styles. ACM Trans. Multimedia Comput. Commun. Appl. 9, 4, Article 01 (August 2019), 20 pages. https://doi.org/0000001.0000001 1 INTRODUCTION With the rise of music streaming services, such as Spotify, Apple Music, and Deezer, music playlists generation became an important research topic [10]. With their smartphones, people have instant access to millions of songs, which can be easily compiled into playlists to be listened anywhere, anytime. There are many activities where people turn to music playlists to help ease the monotony and provide motivation such as, for example, in workout gyms or office spaces. A dichotomy in such scenarios is that while people’s mood gradually changes throughout the activity, the playlist usually circulates impassively over similar songs [4, 7, 16, 17, 19], so there is nothing left to the Authors’ addresses: Marcos Alves de Almeida, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, marcos. [email protected]; Carolina Coimbra Vieira, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, carolcoimbra@ dcc.ufmg.br; Pedro Olmo Stancioli Vaz De Melo, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, olmo@dcc. ufmg.br; Renato Martins Assunção, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil, [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice andthe full citation on the first page. Copyrights for components of this work owned by others than the author(s) must behonored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. 1551-6857/2019/08-ART01 $15.00 https://doi.org/0000001.0000001 ACM Trans. Multimedia Comput. Commun. Appl., Vol. 9, No. 4, Article 01. Publication date: August 2019. 01:2 M. Almeida et al. user but to manually change the current playlist for a more appropriate one. Under workouts, for instance, some users may like to progressively increase the tempo of the songs in the playlist until a given point, where the songs should gradually change to more relaxing ones [2]. Imagine a user who may want to balance out two opposing wishes. For one side, to listen to the songs they love as often as possible, but risking to get tired of them. For the other side, to listen to new and unexpected songs that eventually add to that beloved list. This user want ever new instances of randomly generated playlists with songs revolving around his preferred styles or performers. This task becomes particularly harder if we add the requirement that the playlists should span widely diverse styles in order to accommodate mood fluctuations. The good news is that gradually changing the songs in order to connect significant different genres would naturally yield songs that very likely are not known by the user, which is a desired property for playlists [38]. This scenario is just one illustration of many other situations where a user may desire a playlist with some specific characteristics. Although the quality of a playlist is commonly associated with its homogeneity in terms of the songs’ similarity [4, 7, 16, 19], it is well known that a playlist does not need to be entirely homogeneous if the songs fit in a given context or purpose6 [ , 20, 34, 37]. Indeed, it is hard to imagine a homogeneous playlist that fits well in a wedding or graduation party. These events may be composed by a very heterogeneous group of people in terms of age, cultural background, social status and, consequently, musical taste [14]. Thus, in such events, it is expected that all groups of people can listen to songs of their liking at some point, and a smooth transition between these likely diverse songs is appreciated [4,7, 16, 19]. Based on all the constraints previously discussed, the focus on this paper is the proposal of algorithms to generate random playlists that may be very heterogeneous at the user discretion and satisfying a set of established desired properties. The main goal of the algorithms is to randomly select an ordered list of k songs out of a large music collection. This list has two (or more) specific songs, called anchor songs, pre-selected by the user and potentially of widely different genres. The user also selects the desired number of songs in the playlist. The motivation behind letting the user define the anchor songs of the playlist is that he controls the region of the musicspacehe would like the playlist to be, satisfying his music taste. Also, by defining the number of songs inthe playlist, he implicitly controls its duration. The generated playlist should favor smooth transitions between successive tracks, even if from different styles, and should be randomly generated in order to satisfy the novelty property (e.g. different playlists at each commuting day). Moreover, the method should require minimal user effort in the process and should be able to pass through different genres as fast as the user desires. Finally, the method should be fast and scalable, i.e.,it should be able to generate playlists from very large music collections and in real time. One method to generate heterogeneous playlists with smooth transitions is to simply concatenate different homogeneous playlists. But this is a poor approach, since the sudden and drastic change when switching between the playlists may annoy the users. A better solution would sort the songs in such a way that successive songs present a smooth transition, even when changing between styles [34]. However, this solution may not be feasible since ordering a list of songs to have smooth transitions is similar to the TSP and may not scale with the size of the playlist. Our algorithm should be able to generate smooth and random trajectories between widely spread endpoint songs in a certain music space. For example, one starting from Elvis Presley and ending up with Daft Punk without rough transitions between successive songs. The random aspect guarantees different playlists every time the algorithm is run avoiding boredom. The smoothness aspect creates an atmosphere where emotions are built and developed in a pleasant and steady way. Considering automated generation of random and heterogeneous playlists, there are several aspects that make this a challenging task. First, there is usually a large number of available tracks that can potentially be added to the playlist [7]. If we consider a heterogeneous playlist, this number ACM Trans. Multimedia Comput. Commun. Appl., Vol. 9, No. 4, Article 01. Publication date: August 2019. Random Playlists Smoothly Commuting Between Styles 01:3 can be extremely large, requiring an efficient and scalable algorithm. Second, to better decide which songs should be added to the playlist to satisfy users taste, a well defined similarity measure between tracks is desired. Even thought there are many proposed methods to calculate the similarity between tracks using acoustic characteristics (as timbre, pitch and harmony) and metadata (as tags and popularity), this is a task that yet does not have a standard solution [4, 7, 11]. Third, playlists should maintain smooth transitions between consecutive songs. Unfortunately, similarity- based algorithms, which are the most common approach [4, 7], generate smooth transitions while maximizing playlist homogeneity. This compromises the diversity and serendipity of the tracks, which are reported as desired properties for playlists [4, 7, 38]. Fourth, there is the problem of clustering the songs and coherently mapping them into a music space, since a heterogeneous playlist should pass through significantly different genres in an orderly and smooth way.

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