@article {2024,
	title = {Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey},
	journal = {ACM Computing Surveys},
	year = {2025},
	abstract = {Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.},
	doi = {https://arxiv.org/abs/2402.17467},
	url = {https://arxiv.org/abs/2402.17467},
	author = {Dinh-Viet-Toan Le and L. Bigo and M. Keller and D. Herremans}
}
@article {2019,
	title = {nnAudio: An on-the-fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolution Neural Networks},
	journal = {IEEE Access},
	year = {2020},
	abstract = {Converting time domain waveforms to frequency domain spectrograms is typically considered to be a prepossessing step done before model training. This approach, however, has several drawbacks. First, it takes a lot of hard disk space to store different frequency domain representations. This is especially true during the model development and tuning process, when exploring various types of spectrograms for optimal performance. Second, if another dataset is used, one must process all the audio clips again before the network can be retrained. In this paper, we integrate the time domain to frequency domain conversion as part of the model structure, and propose a neural network based toolbox, nnAudio, which leverages 1D convolutional neural networks to perform time domain to frequency domain conversion during feed-forward. It allows on-the-fly spectrogram generation without the need to store any spectrograms on the disk. This approach also allows back-propagation on the waveforms-to-spectrograms transformation layer, which implies that this transformation process can be made trainable, and hence further optimized by gradient descent. nnAudio reduces the waveforms-to-spectrograms conversion time for 1,770 waveforms (from the MAPS dataset) from 10.64 seconds with librosa to only 0.001 seconds for Short-Time Fourier Transform (STFT), 18.3 seconds to 0.015 seconds for Mel spectrogram, 103.4 seconds to 0.258 for constant-Q transform (CQT), when using GPU on our DGX work station with CPU: Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz Tesla v100 32Gb GPUs. (Only 1 GPU is being used for all the experiments.) We also further optimize the existing CQT algorithm, so that the CQT spectrogram can be obtained without aliasing in a much faster computation time (from 0.258 seconds to only 0.001 seconds).},
	doi = {10.1109/ACCESS.2020.3019084},
	url = {https://ieeexplore.ieee.org/document/9174990},
	author = {K.W. Cheuk and H. Anderson and K. Agres and D. Herremans}
}
@conference {2019,
	title = {nnAudio: A PyTorch Audio Processing Tool Using 1D Convolution neural networks},
	booktitle = {ISMIR - Late Breaking Demo},
	year = {2019},
	month = {10/2019},
	address = {Delft, The Netherlands},
	author = {K.W. Cheuk and K. Agres and D. Herremans}
}
@conference {2019,
	title = {A novel music-based game with motion capture to support cognitive and motor function in the elderly},
	booktitle = {IEEE Conference on Games},
	year = {2019},
	publisher = {IEEE },
	organization = {IEEE },
	address = {London, UK},
	abstract = {This paper presents a novel game prototype that uses music and motion detection as preventive medicine for the elderly.
Given the aging populations around the globe, and the limited resources and staff able to care for these populations, eHealth solutions are becoming increasingly important, if not crucial, additions to modern healthcare and preventive medicine. Furthermore, because compliance rates for performing physical exercises are often quite low in the elderly, systems able to motivate and engage this population are a necessity. Our prototype uses music not only to engage listeners, but also to leverage the efficacy of music to improve mental and physical wellness. The game is based on a memory task to stimulate cognitive function, and requires users to perform physical gestures to mimic the playing of different musical instruments. To this end, the Microsoft Kinect sensor is used together with a newly developed gesture detection module in order to process users{\textquoteright} gestures. The resulting prototype system supports both cognitive functioning and physical strengthening in the elderly. },
	author = {K. Agres and S. Lui and D. Herremans}
}
@article {128,
	title = {A Novel Interface for the Graphical Analysis of Music Practice Behaviours},
	journal = {Frontiers in Psychology - Human-Media Interaction},
	volume = {9},
	year = {2018},
	month = {11/2018},
	chapter = {2292},
	doi = {10.3389/fpsyg.2018.02292},
	url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2018.02292/full},
	author = {J. Sokolovskis and D. Herremans and E. Chew}
}
