Song Preference CLassification Dataset for Gen Z

This dataset contains audio recordings of 12 different accents across the UK: Northern Ireland, Scotland, Wales, North East England, North West England, Yorkshire and Humber, East Midlands, West Midlands, East of England, Greater London, South East England, South West England. We split the data into a Male: Female ratio of 1:1. The audio dataset was compiled using opensource YouTube videos and it a collation of different accents, the audio files were trimmed for uniformity. The Audio files are of length 30 seconds, with the first 5 seconds and last 5 seconds of the signal being blank. We also resample the audio signals at 8 kHz, again for uniformity and to remove any noise present in the audio signals whilst retaining the underlying characteristics. The intended application of this dataset was to be used in conjunction with a deep neural network for accent and gender classification tasks.

This dataset was recorded for an experimentation looking into applying machine learning techniques for the task of classifying song preference amongst generation Z (18 to 24 years) participants. We define a labelling system corresponding to specific songs with 5 ratings: hate, dislike, neutral, like and love. The songs used for this experiment were chosen due their success for various awards, such as the BRIT awards (BRIT), Mercury Prize (MERC), Rolling Stone most influential albums (ROLS). They are as shown:

    S1: One Kiss by Calvin Harris and Dua Lipa (BRIT)

    S2: Don't Delete the Kisses by Wolf Alice MERC)

    S3: Money by Pink Floyd (ROLS)

    S4: Shotgun by George Ezra (BRIT)

    S5: Location by Dave (MERC)

    S6: Smells Like Teen Spirit by Nirvana (ROLS)

    S7: God's Plan by Drake (BRIT)

    S8: Breezeblocks by alt-J (MERC)

    S9: Lucy In The Sky With Diamonds by The Beatles (ROLS)

    S10: Thank U, Next by Ariana Grande (BRIT)

    S11: Shutdown by Skepta (MERC)

    S12: Billie Jean by Micheal Jackson (ROLS)

 A Unicorn Hybrid Black was used for recording the EEG data from the participants whilst they were played the control songs listed above. For each of the 12 total song played to a participant during the experiment, there were 8 EEG lead recordings measured of length 20 seconds, with the first 5 seconds and the last 5 seconds being blank for control purposes. The EEG signals were sampled at 250 Hz by the Unicorn Hybrid Black devices, which also filtered the signals to be between 2Hz to 30 Hz in order to remove any noise recorded during the experimentation. There are approximately 5000 data points per reading of a given song, with there being 12 songs played to a total of 10 participants.