What is HHDS? HHDS is a reduced compilation of Hip Hop songs, used to train a Convolutional Neural Network (CNN) for audio source separation in [1], built on top of the DeepConvSep framework [2] developed at the Music Technology Group (MTG), Universitat Pompeu Fabra. The structure of HHDS follows the convention of DSD100 [3] (Demixing Secrets Dataset). HHDS contains the separated tracks for the categories of bass, drums, vocals and others in monophonic WAV les with a sampling rate of 44100Hz. The mixture is calculated by normalizing the sum of the tracks. The main difference with respect to DSD100 is that in HHDS there are HipHop songs only, instead of many different genres. The total number of songs is 18, from which 13 are used for training and 5 are used for evaluation. A detailed list of the songs included in the dataset can be found inside the .zip file provided. The reader can also find the code for this dataset in the DeepConvSep repository in the path examples/hiphopss. References [1] "A Deep Learning Approach to Source Separation and Remixing of HipHop music", Héctor Martel, Undergraduate Thesis, Universitat Pompeu Fabra 2016-2017. [2] DeepConvSep repository on GitHub: https://github.com/MTG/DeepConvSep [3] Demixing Secrets Dataset (DSD100), SiSEC2016: http://liutkus.net/DSD100.zip