@conference {2025,
	title = {ImprovNet: Generating Controllable Musical Improvisations with Iterative Corruption Refinement},
	booktitle = {Proceedings of IJCNN},
	year = {2025},
	address = {Rome, Italy},
	abstract = {Deep learning has enabled remarkable advances in style transfer across various domains, offering new possibilities for creative content generation. However, in the realm of symbolic music, generating controllable and expressive performance-level style transfers for complete musical works remains challenging due to limited datasets, especially for genres such as jazz, and the lack of unified models that can handle multiple music generation tasks. This paper presents ImprovNet, a transformer-based architecture that generates expressive and controllable musical improvisations through a self-supervised corruption-refinement training strategy. ImprovNet unifies multiple capabilities within a single model: it can perform cross-genre and intra-genre improvisations, harmonize melodies with genre-specific styles, and execute short prompt continuation and infilling tasks. The model{\textquoteright}s iterative generation framework allows users to control the degree of style transfer and structural similarity to the original composition. Objective and subjective evaluations demonstrate ImprovNet{\textquoteright}s effectiveness in generating musically coherent improvisations while maintaining structural relationships with the original pieces. The model outperforms Anticipatory Music Transformer in short continuation and infilling tasks and successfully achieves recognizable genre conversion, with 79\% of participants correctly identifying jazz-style improvisations. Our code and demo page can be found at this https URL (https://github.com/keshavbhandari/improvnet).},
	doi = {10.1109/IJCNN64981.2025.11229312},
	url = {https://arxiv.org/abs/2502.04522},
	author = {K. Bhandari and S. Chang and T. Lu and F. R. Enus and L. B. Bradshaw and D. Herremans and S. Colton}
}
@conference {2020,
	title = {The impact of Audio input representations on neural network based music transcription},
	booktitle = {Proceedings of the International Joint Conference on Neural Networks (IJCNN)},
	year = {2020},
	month = {07/2020},
	address = {Glasgow},
	abstract = {This paper thoroughly analyses the effect of different input representations on polyphonic multi-instrument music transcription. We use our own GPU based spectrogram extraction tool, nnAudio, to investigate the influence of using a linear-frequency spectrogram, log-frequency spectrogram, Mel spectrogram, and constant-Q transform (CQT). Our results show that a 8.33\% increase in transcription accuracy and a 9.39\% reduction in error can be obtained by choosing the appropriate input representation (log-frequency spectrogram with STFT window length 4,096 and 2,048 frequency bins in the spectrogram) without changing the neural network design (single layer fully connected). Our experiments also show that Mel spectrogram is a compact representation for which we can reduce the number of frequency bins to only 512 while still keeping a relatively high music transcription accuracy.},
	url = {https://arxiv.org/abs/2001.09989},
	author = {K.W. Cheuk and K. Agres and D. Herremans}
}
@inbook {2019,
	title = {The impact of musical structure on enjoyment and absorptive listening states in trance music},
	booktitle = {Music and Consciousness 2 - Worlds, Practices, Modalities},
	year = {2019},
	publisher = {Oxford University Press},
	organization = {Oxford University Press},
	edition = {Editors: Ruth Herbert, David Clarke, and Eric Clarke},
	address = {Oxfort, UK},
	abstract = {https://books.google.com.sg/books?id=DCOQDwAAQBAJ\&pg=PA254\&lpg=PA254\&dq=The+impact+of+musical+structure+on+enjoyment+and+absorptive+listening+states+in+trance+music\&source=bl\&ots=FsWlNXsys-\&sig=ACfU3U0K6dFJD7zbC917zG6HRFXLxVSdvQ\&hl=en\&sa=X\&ved=2ahUKEwjd05e7jdXkAhU_4nMBHe7bBQgQ6AEwBnoECAkQAQ$\#$v=onepage\&q=The\%20impact\%20of\%20musical\%20structure\%20on\%20enjoyment\%20and\%20absorptive\%20listening\%20states\%20in\%20trance\%20music\&f=false},
	issn = {9780198804352},
	url = {https://global.oup.com/academic/product/music-and-consciousness-2-9780198804352?cc=us\&lang=en\&$\#$},
	author = {K. Agres and L. Bigo and D. Herremans}
}
@proceedings {86,
	title = {IMMA-Emo: A Multimodal Interface for Visualising Score- and Audio-synchronised Emotion Annotations},
	year = {2017},
	month = {08/2017},
	address = {London, UK},
	author = {D. Herremans and S. Yang and C.-H. Chuan and M. Barthet and E. Chew}
}
