@article {2023,
	title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information},
	journal = {Sensors},
	volume = {23},
	year = {2023},
	month = {12/2022},
	pages = {1-23},
	abstract = {Abstract: Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence-arousal space. Four additional songs were selected from the DEAM dataset to act as a benchmark in this study and filter out low quality ratings. A total of 452 participants participated in annotating the dataset, with 277 participants remaining after thoroughly cleaning the dataset. Their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.},
	keywords = {emotion prediction; music; music emotion dataset; affective computing},
	doi = {https://doi.org/10.3390/s23010382},
	url = {https://www.mdpi.com/1424-8220/23/1/382},
	author = {En Yan Koh and Kin Wai Cheuk and Kwan Yee Heung and Kat R. Agres and Dorien Herremans}
}
@article {9999,
	title = {nnAudio:~~An on-the-fly GPU Audio toSpectrogram Conversion Toolbox Using 1D Convolutional Neural Networks},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	month = {09/2020},
	pages = {161981-162003},
	type = {Journal paper},
	abstract = {In this paper, we present nnAudio, a new neural network based audio processing framework with graphics processing unit (GPU) support which leverages 1D convolutional neural networks to perform time domain to frequency domain conversion. It allows on-the-fly spectrogram extraction due to its fast speed, without the need to store any spectrograms on the disk. Moreover, this approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, and hence, the transformation process can be made trainable, further optimizing the waveform-to-spectrogram transformation for the specific task that the neural network is trained on. All spectrogram implementations scale as Big-O of linear time with respect to the input length. nnAudio, however, leverages the compute unified device architecture (CUDA) of 1D convolutional neural network from PyTorch, its short-time Fourier transform (STFT), Mel spectrogram, and constant-Q transform (CQT) implementations are an order of magnitude faster than other implementations using only central processing unit (CPU). We tested our framework on three different machines with NVIDIA GPUs, and our framework significantly reduces the spectrogram extraction time from the order of seconds (using a popular python library librosa) to only the order of milliseconds when the audio recordings are of the same length. When applying nnAudio to variable input audio lengths, an average of 11.5 hours are required to extract 34 spectrogram types with different parameters from the MusicNet data set using librosa. An average of 2.8 hours is required for nnAudio, which is still four times faster than librosa. nnAudio also outperforms existing GPU processing libraries such as Kapre and torchaudio in terms of processing speed. },
	doi = {doi: 10.1109/ACCESS.2020.3019084},
	url = {https://ieeexplore.ieee.org/document/9174990},
	author = {Kin Wai Cheuk and Hans Anderson and Kat Agres and Dorien Herremans}
}
