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From raw audio to a seamless mix : creating an automated DJ system for drum and bass

Len Vande Veire (UGent) and Tijl De Bie (UGent)
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Abstract
We present the open-source implementation of the first fully automatic and comprehensive DJ system, able to generate seamless music mixes using songs from a given library much like a human DJ does. The proposed system is built on top of several enhanced music information retrieval (MIR) techniques, such as for beat tracking, downbeat tracking, and structural segmentation, to obtain an understanding of the musical structure. Leveraging the understanding of the music tracks offered by these state-of-the-art MIR techniques, the proposed system surpasses existing automatic DJ systems both in accuracy and completeness. To the best of our knowledge, it is the first fully integrated solution that takes all basic Wing best practices into account, from beat and downbeat matching to identification of suitable cue points, determining a suitable cross-fade profile and compiling an interesting playlist that trades off innovation with continuity. To make this possible, we focused on one specific sub-genre of electronic dance music, namely Drum and Bass. This allowed us to exploit genre-specific properties, resulting in a more robust performance and tailored mixing behavior. Evaluation on a corpus of 160 Drum and Bass songs and an additional hold-out set of 220 songs shows that the used MIR algorithms can annotate 91% of the songs with fully correct annotations (tempo, beats, downbeats, and structure for cue points). On these songs, the proposed song selection process and the implemented Wing techniques enable the system to generate mixes of high quality, as confirmed by a subjective user test in which 18 Drum and Bass fans participated.
Keywords
BEAT TRACKING, MUSIC SIGNALS, SCALE, DJ, Drum and Bass, MIR, Computational creativity, Machine learning

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MLA
Vande Veire, Len, and Tijl De Bie. “From Raw Audio to a Seamless Mix : Creating an Automated DJ System for Drum and Bass.” EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING (2018): 1–21. Print.
APA
Vande Veire, L., & De Bie, T. (2018). From raw audio to a seamless mix : creating an automated DJ system for drum and bass. EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, 1–21.
Chicago author-date
Vande Veire, Len, and Tijl De Bie. 2018. “From Raw Audio to a Seamless Mix : Creating an Automated DJ System for Drum and Bass.” Eurasip Journal on Audio Speech and Music Processing: 1–21.
Chicago author-date (all authors)
Vande Veire, Len, and Tijl De Bie. 2018. “From Raw Audio to a Seamless Mix : Creating an Automated DJ System for Drum and Bass.” Eurasip Journal on Audio Speech and Music Processing: 1–21.
Vancouver
1.
Vande Veire L, De Bie T. From raw audio to a seamless mix : creating an automated DJ system for drum and bass. EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING. London: Springeropen; 2018;1–21.
IEEE
[1]
L. Vande Veire and T. De Bie, “From raw audio to a seamless mix : creating an automated DJ system for drum and bass,” EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, pp. 1–21, 2018.
@article{8577611,
  abstract     = {We present the open-source implementation of the first fully automatic and comprehensive DJ system, able to generate seamless music mixes using songs from a given library much like a human DJ does. The proposed system is built on top of several enhanced music information retrieval (MIR) techniques, such as for beat tracking, downbeat tracking, and structural segmentation, to obtain an understanding of the musical structure. Leveraging the understanding of the music tracks offered by these state-of-the-art MIR techniques, the proposed system surpasses existing automatic DJ systems both in accuracy and completeness. To the best of our knowledge, it is the first fully integrated solution that takes all basic Wing best practices into account, from beat and downbeat matching to identification of suitable cue points, determining a suitable cross-fade profile and compiling an interesting playlist that trades off innovation with continuity. To make this possible, we focused on one specific sub-genre of electronic dance music, namely Drum and Bass. This allowed us to exploit genre-specific properties, resulting in a more robust performance and tailored mixing behavior. Evaluation on a corpus of 160 Drum and Bass songs and an additional hold-out set of 220 songs shows that the used MIR algorithms can annotate 91% of the songs with fully correct annotations (tempo, beats, downbeats, and structure for cue points). On these songs, the proposed song selection process and the implemented Wing techniques enable the system to generate mixes of high quality, as confirmed by a subjective user test in which 18 Drum and Bass fans participated.},
  articleno    = {13},
  author       = {Vande Veire, Len and De Bie, Tijl},
  issn         = {1687-4722},
  journal      = {EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING},
  keywords     = {BEAT TRACKING,MUSIC SIGNALS,SCALE,DJ,Drum and Bass,MIR,Computational creativity,Machine learning},
  language     = {eng},
  pages        = {13:1--13:21},
  publisher    = {Springeropen},
  title        = {From raw audio to a seamless mix : creating an automated DJ system for drum and bass},
  url          = {http://dx.doi.org/10.1186/s13636-018-0134-8},
  year         = {2018},
}

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