@conference {2023,
	title = {Mustango: Toward Controllable Text-to-Music Generation},
	booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). pages 8293{\textendash}8316},
	year = {2024},
	publisher = {ACM},
	organization = {ACM},
	address = {Mexico City, Mexico},
	abstract = {With recent advancements in text-to-audio and text-to-music based on latent diffusion models, the quality of generated content has been reaching new heights. The controllability of musical aspects, however, has not been explicitly explored in text-to-music systems yet. In this paper, we present Mustango, a music-domain-knowledge-inspired text-to-music system based on diffusion, that expands the Tango text-to-audio model. Mustango aims to control the generated music, not only with general text captions, but from more rich captions that could include specific instructions related to chords, beats, tempo, and key. As part of Mustango, we propose MuNet, a Music-Domain-Knowledge-Informed UNet sub-module to integrate these music-specific features, which we predict from the text prompt, as well as the general text embedding, into the diffusion denoising process. To overcome the limited availability of open datasets of music with text captions, we propose a novel data augmentation method that includes altering the harmonic, rhythmic, and dynamic aspects of music audio and using state-of-the-art Music Information Retrieval methods to extract the music features which will then be appended to the existing descriptions in text format. We release the resulting MusicBench dataset which contains over 52K instances and includes music-theory-based descriptions in the caption text. Through extensive experiments, we show that the quality of the music generated by Mustango is state-of-the-art, and the controllability through music-specific text prompts greatly outperforms other models in terms of desired chords, beat, key, and tempo, on multiple datasets.},
	keywords = {genAI, music generation, text-to-music},
	doi = {https://aclanthology.org/2024.naacl-long.pdf},
	url = {https://arxiv.org/abs/2311.08355},
	author = {Jan Melechovsky and Zixun Guo and Deepanway Ghosal and Navonil Majumder and Dorien Herremans and Soujanya Poria}
}
@article {2021,
	title = {Deep Neural Network Based Respiratory Pathology Classification Using Cough Sounds},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	pages = {5555},
	abstract = {Intelligent systems are transforming the world, as well as our healthcare system. We propose a deep learning-based cough sound classification model that can distinguish between children with healthy versus pathological coughs such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI). In order to train a deep neural network model, we collected a new dataset of cough sounds, labelled with clinician{\textquoteright}s diagnosis. The chosen model is a bidirectional long-short term memory network (BiLSTM) based on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting trained model when trained for classifying two classes of coughs -- healthy or pathology (in general or belonging to a specific respiratory pathology), reaches accuracy exceeding 84\% when classifying cough to the label provided by the physicians{\textquoteright} diagnosis. In order to classify subject{\textquoteright}s respiratory pathology condition, results of multiple cough epochs per subject were combined. The resulting prediction accuracy exceeds 91\% for all three respiratory pathologies. However, when the model is trained to classify and discriminate among the four classes of coughs, overall accuracy dropped: one class of pathological coughs are often misclassified as other. However, if one consider the healthy cough classified as healthy and pathological cough classified to have some kind of pathologies, then the overall accuracy of four class model is above 84\%. A longitudinal study of MFCC feature space when comparing pathological and recovered coughs collected from the same subjects revealed the fact that pathological cough irrespective of the underlying conditions occupy the same feature space making it harder to differentiate only using MFCC features.},
	doi = {10.3390/s21165555},
	url = {https://www.mdpi.com/1424-8220/21/16/5555/htm},
	author = {Balamurali B T and Hwan Ing Hee and Saumitra Kapoor and Oon Hoe Teoh and Sung Shin Teng and Khai Pin Lee and Dorien Herremans and Jer Ming Chen}
}
@conference {2019,
	title = {Midi Miner {\textendash} A Python library for tonal tension and track classification},
	booktitle = {ISMIR - Late Breaking Demo},
	year = {2019},
	month = {10/2019},
	address = {Delft, The Netherlands},
	author = {Rui Guo and Dorien Herremans and Thor Magnusson}
}
