Playlist Generation Research Articles

An intelligent music playlist generator based on the time parameter with artificial neural networks

A music hobbyist listens to different types of music at different times of the day. Thus, an automatic music playlist generator that can adjust to the hobbyist’s daily activities on this basis is necessary in order to generate the appropriate music to suit the user’s current activity, whether it is working or driving. Although existing research has introduced various music playlist generators, there is yet a system that generates the music playlist based on time. Hence, in this paper, we present a music playlist generation system, which provides an automatic and personalized music playing service based on the time parameter. This system represents the characteristics of music from features extracted out of both the music’s symbolic form and wave data. The kernel of this system is based on a modified artificial neural network. The user’s music rating history and the associated time stamps in the user’s profile constitute the training data of the modified artificial neural networks. A collaborative method has also been proposed to reduce the effect of the cold start problem upon system initialization. A series of experiments have been carried out to demonstrate the performance of this system.

A fuzzy framework for defining dynamic playlist generation heuristics

We study a dynamic way of automatically generating sequences of songs in this paper. Starting from a given seed song, a sequence is generated on-the-fly while listening to the music. During this process, the user can express his or her dislike for the currently played song by pressing a skip button. When the current song is finished or skipped, a heuristic is used to choose the next song to be played. We introduce fuzzy set theory as a formalism for defining such heuristics, because this allows us to make the definitions systematic, formal, and intuitively clear. By doing this, we obtain a general unified framework that includes all heuristics introduced previously by other authors. We compare these existing heuristics with several novel ones by means of extensive experimental evaluations. Moreover, we demonstrate that the presented formal framework can be used to easily generate variations of a heuristic that perform significantly better under specific circumstances.

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...

Fast generation of audio signatures to describe iTunes libraries

iTunes Signature Maker (iTSM) uses a feature-driven audio editing algorithm to rapidly generate a short sonic signature of an iTunes music library. iTSM stitches together small segments of songs, driving a concatenative algorithm with spectral features intrinsic to the audio files themselves and with environmental features which describe how those files have been used. This paper relates iTSM to projects in algorithmic playlist generation and music summarization and then describes the software's implementation in relation to the project's objectives: accessibility to a broad audience, accurate representation of users' musical preferences, and interesting and enjoyable results. The project is evaluated in terms of these objectives using both statistical data and anecdotal feedback collected from users.

The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future

Computer Music Journal, 28:2, pp. 12–23, Summer 2004 2004 Massachusetts Institute of Technology. Music Information Retrieval (MIR) is a multidisciplinary research endeavor that strives to develop innovative content-based searching schemes, novel interfaces, and evolving networked delivery mechanisms in an effort to make the world’s vast store of music accessible to all. Some teams are developing ‘‘Query-by-Singing’’ and ‘‘Query-by-Humming’’ systems that allow users to interact with their respective music search engines via queries that are sung or hummed into a microphone (e.g., Birmingham et al. 2001; Haus and Pollastri 2001). ‘‘Queryby-Note’’ systems are also being developed wherein searchers construct queries consisting of pitch and/or rhythm information (e.g., Pickens 2000; Doraisamy and Ruger 2002). Input methods for Queryby-Note systems include symbolic interfaces as well as both physical (MIDI) and virtual (Javabased) keyboards. Some teams are working on ‘‘Query-by-Example’’ systems that take prerecorded music in the form of CD or MP3 tracks as their query input (e.g., Haitsma and Kalker 2002; Harb and Chen 2003). The development of comprehensive music recommendation and distribution systems is a growing research area (e.g., Logan 2002; Pauws and Eggen 2002). The automatic generation of playlists for use in personal music systems, based on a wide variety of user-defined criteria, is the goal of this branch of MIR research. Other groups are investigating the creation of music analysis systems to assist those in the musicology and music theory communities (e.g., Barthelemy and Bonardi 2001; Kornstadt 2001). Overviews of MIR’s interdisciplinary research areas can be found in Downie (2003), Byrd and Crawford (2002), and Futrelle and Downie (2002). This article begins with an overview of the current scientific problem facing MIR research. Entitled ‘‘Current Scientific Problem,’’ the opening section also provides a brief explication of the Text Retrieval Conference (TREC) evaluation paradigm that has come to play an important role in the community’s thinking about the testing and evaluation of MIR systems. The sections which follow, entitled ‘‘Data Collection Method’’ and ‘‘Emergent Themes and Commentary,’’ report upon the findings of the Music Information Retrieval (MIR)/ Music Digital Library (MDL) Evaluation Frameworks Project with issues surrounding the creation of a TREC-like evaluation paradigm for MIR as the central focus. ‘‘Building a TREC-Like Test Collection’’ follows next and highlights the progress being made concerning the establishment of the necessary test collections. The ‘‘Summary and Future Research’’ section concludes this article and highlights some of the key challenges uncovered that require further investigation.

Recognition and analysis of audio for copyright protection: The RAA project

AbstractAutomatic generation of play lists for commercial broadcast radio stations has become a major research topic. Audio identification systems have been around for a while, and they show good performance for clean audio files. However, songs transmitted by commercial radio stations are highly distorted to cause greater impact on the casual listener. This impact helps increase the probability that the listener will stay tuned in, but the price we have to pay is a severe modification in the audio itself. This causes the failure of traditional identification systems. Another problem is the fact that songs are never played from the beginning to the end. Actually, they are put on the air several seconds after their real beginning and almost always under the voice of a speaker. The same thing happens at the end. In this article, we present the RAA project, which was conceived to deal with real broadcast audio problems. The idea behind this project is to extract automatically an audio fingerprint (the so‐called AudioDNA) that identifies the fragment of audio. This AudioDNA has to be robust enough to appear almost the same under several degrees of distortion. Once this AudioDNA is extracted from the broadcast audio, a matching algorithm is able to find its fragments inside a database. With this approach, the system can find not only a whole song but also small fragments of it, even with high distortion caused by broadcast (and DJ) manipulations.

Realization and User Evaluation of an Automatic Playlist Generator

An automatic music playlist generator called PATS (Personalized Automatic Track Selection) creates playlists that aim at suiting a particular listening situation. It uses dynamic clustering in which songs are grouped based on a weighted attribute-value similarity measure. An inductive learning algorithm is used to reveal the weights for attribute-values using user preference feedback. In a controlled user experiment, the quality of PATS-generated and randomly assembled playlists for jazz music was assessed in two listening situations. The two listening situations were “listening to soft music” and “listening to lively music.” Playlist quality was measured by precision (songs that suit the listening situation), coverage (songs that suit the listening situation but that were not already contained in previous playlists) and a rating score. Results showed that PATS playlists contained increasingly more preferred music (increasingly higher precision), covered more preferred music in the collection (higher coverage), and were rated higher than randomly assembled playlists.