102 research outputs found
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion
Research on deep learning-powered voice conversion (VC) in speech-to-speech
scenarios is getting increasingly popular. Although many of the works in the
field of voice conversion share a common global pipeline, there is a
considerable diversity in the underlying structures, methods, and neural
sub-blocks used across research efforts. Thus, obtaining a comprehensive
understanding of the reasons behind the choice of the different methods in the
voice conversion pipeline can be challenging, and the actual hurdles in the
proposed solutions are often unclear. To shed light on these aspects, this
paper presents a scoping review that explores the use of deep learning in
speech analysis, synthesis, and disentangled speech representation learning
within modern voice conversion systems. We screened 621 publications from more
than 38 different venues between the years 2017 and 2023, followed by an
in-depth review of a final database consisting of 123 eligible studies. Based
on the review, we summarise the most frequently used approaches to voice
conversion based on deep learning and highlight common pitfalls within the
community. Lastly, we condense the knowledge gathered, identify main challenges
and provide recommendations for future research directions
Music-STAR: a Style Translation system for Audio-based Rearrangement
Music style translation has recently gained attention among music processing
studies. It aims to generate variations of existing music pieces by altering the style-variant characteristics of the original music piece, while content such as the melody
remains unchanged. These alterations could involve timbre translation, reharmonization,
or music rearrangement.
In this thesis, we plan to address music rearrangement, focusing on instrumentation, by processing waveforms of two-instrument pieces. Previous studies have achieved promising results utilizing time-frequency and symbolic music representations. Music translation on raw audio has also been investigated using single-instrument pieces. Although processing raw audio is more challenging, it embodies more detailed information about the performance, timbre, and dynamics of a music piece. To this end, we introduce Music-STAR, the first audio-based model that can transform the instruments of a multi-track piece into another set of instruments, resulting in a rearranged piece
Zero-shot Singing Technique Conversion
In this paper we propose modifications to the neural network framework, AutoVC for the task of singing technique conversion. This includes utilising a pretrained singing technique encoder which extracts technique information, upon which a decoder is conditioned during training. By swapping out a source singer’s technique information for that of the target’s during conversion, the input spectrogram is reconstructed with the target’s technique. We document the beneficial effects of omitting the latent loss, the importance of sequential training, and our process for fine-tuning the bottleneck. We also conducted a listening study where participants rate the specificity of technique-converted voices as well as their naturalness. From this we are able to conclude how effective the technique conversions are and how different conditions affect them, while assessing the model’s ability to reconstruct its input data
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
CLN-VC: Text-Free Voice Conversion Based on Fine-Grained Style Control and Contrastive Learning with Negative Samples Augmentation
Better disentanglement of speech representation is essential to improve the
quality of voice conversion. Recently contrastive learning is applied to voice
conversion successfully based on speaker labels. However, the performance of
model will reduce in conversion between similar speakers. Hence, we propose an
augmented negative sample selection to address the issue. Specifically, we
create hard negative samples based on the proposed speaker fusion module to
improve learning ability of speaker encoder. Furthermore, considering the
fine-grain modeling of speaker style, we employ a reference encoder to extract
fine-grained style and conduct the augmented contrastive learning on global
style. The experimental results show that the proposed method outperforms
previous work in voice conversion tasks.Comment: Accepted by the 21st IEEE International Symposium on Parallel and
Distributed Processing with Applications (IEEE ISPA 2023
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