@proceedings {2025,
	title = {SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering},
	year = {2026},
	address = {South Korea},
	abstract = {Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster{\textquoteright}s enhanced outputs over other baselines. The model and demo samples are available through https://github.com/AMAAI-Lab/SonicMaster.},
	url = {https://arxiv.org/abs/2508.03448},
	author = {J. Melechovsky and A. Mehrish and A. Roy and D. Herremans}
}
@conference {2024,
	title = {Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {With rapid globalization, the need to build inclusive and representative speech technology cannot be overstated. Accent is an important aspect of speech that needs to be taken into consideration while building inclusive speech synthesizers. Inclusive speech technology aims to erase any biases towards specific groups, such as people of certain accent. We note that state-of-the-art Text-to-Speech (TTS) systems may currently not be suitable for all people, regardless of their background, as they are designed to generate high-quality voices without focusing on accent. In this paper, we propose a TTS model that utilizes a Multi-Level Variational Autoencoder with adversarial learning to address accented speech synthesis and conversion in TTS, with a vision for more inclusive systems in the future. We evaluate the performance through both objective metrics and subjective listening tests. The results show an improvement in accent conversion ability compared to the baseline.},
	url = {https://arxiv.org/abs/2406.01018},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
@conference {2022,
	title = {Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {Accent plays a significant role in speech communication, influencing understanding capabilities and also conveying a person{\textquoteright}s identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker{\textquoteright}s speech that is converted to any desired target accent. Our thorough experiments validate the effectiveness of our proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the ability to manipulate accents in the synthesized speech and provide a promising avenue for future accented TTS research.},
	url = {https://arxiv.org/abs/2211.03316},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
@conference {2024,
	title = {DART: Disentanglement of Accent and Speaker Representation in Multispeaker Text-to-Speech},
	booktitle = {Audio Imagination: NeurIPS 2024 Workshop},
	year = {2024},
	address = {Vancouver},
	abstract = {Recent advancements in Text-to-Speech (TTS) systems have enabled the generation of natural and expressive speech from textual input. Accented TTS aims to enhance user experience by making the synthesized speech more relatable to minority group listeners, and useful across various applications and context. 
Speech synthesis can further be made more flexible by allowing users to choose any combination of speaker identity and accent, resulting in a wide range of personalized speech outputs.
Current models struggle to disentangle speaker and accent representation, making it difficult to accurately imitate different accents while maintaining the same speaker characteristics. We propose a novel approach to disentangle speaker and accent representations using multi-level variational autoencoders (ML-VAE) and vector quantization (VQ) to improve flexibility and enhance personalization in speech synthesis.
Our proposed method addresses the challenge of effectively separating speaker and accent characteristics, enabling more fine-grained control over the synthesized speech.
Code and speech samples are publicly available (https://hoglord.github.io/DART/). },
	url = {https://arxiv.org/abs/2410.13342},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
@conference {2023,
	title = {Learning accent representation with multi-level VAE towards controllable speech synthesis},
	booktitle = { IEEE Spoken Language Technology (SLT) Workshop},
	year = {2023},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Doha, Quatar},
	abstract = {Accent is a crucial aspect of speech that helps define one{\textquoteright}s identity. We note that the state-of-the-art Text-to-Speech (TTS) systems can achieve high-quality generated voice, but still lack in terms of versatility and customizability. Moreover, they generally do not take into account accent, which is an important feature of speaking style. In this work, we utilize the concept of Multi-level VAE (ML-VAE) to build a control mechanism that aims to disentangle accent from a reference accented speaker; and to synthesize voices in different accents such as English, American, Irish, and Scottish. The proposed framework can also achieve high-quality accented voice generation for multi-speaker setup, which we believe is remarkable. We investigate the performance through objective metrics and conduct listening experiments for a subjective performance assessment. We showed that the proposed method achieves good performance for naturalness, speaker similarity, and accent similarity.},
	author = {J. Melechovsky and A. Mehrish and D. Herremans and B. Sisman}
}
