50 citations · 75 across the 26 of their papers we have counts for
31 papers
Music Source Separation via Stem Discovery
V. Valtteri Kallinen, Eloi Moliner, Lauri Juvela +1
Music source separation (MSS) methods aim to extract stems from music mixtures, which is important, for example, in karaoke, music remixing, and pedagogical applications. While ear…
EmphTTS: an emphasis-control TTS with reinforcement learning
Zirui Li, Rech Silas, Lauri Juvela +2
Generating controllable and human-like emphasis remains an open challenge in text-to-speech, even when explicit emphasis control signals are provided in the text input, limiting th…
Music Restoration via Latent Operator Optimization and Diffusion Model Priors
Michal Švento, Eloi Moliner, Valtteri Kallinen +3
Music restoration seeks to recover a clean signal from an observed recording degraded by an unknown effect, distortion, or corruption. Existing systems often rely on paired trainin…
Frequency-Aware Self-Supervised Music Representation Learning
Yicheng Gu, Junan Zhang, Jerry Li +2
Self-supervised learning (SSL) has emerged as an essential paradigm for music information retrieval (MIR). While current SSL models achieve state-of-the-art performance across vari…
Aliasing-Free Neural Audio Synthesis
Yicheng Gu, Junan Zhang, Chaoren Wang +3
In neural audio synthesis, neural vocoders and codecs are models that reconstruct waveforms from acoustic and latent representations, which are essential to the resulting audio qua…
Nord-Parl-TTS: Finnish and Swedish TTS Dataset from Parliament Speech
Zirui Li, Jens Edlund, Yicheng Gu +3
Text-to-speech (TTS) development is limited by scarcity of high-quality, publicly available speech data for most languages outside a few high-resource languages. We present Nord-Pa…
Deep Regularized RNNs for Virtual Analog Modeling
V. Valtteri Kallinen, Lauri Juvela, Thom Sherson
Virtual analog (VA) modeling methods seek to emulate analog audio hardware using digital signal processing (DSP). Modeling approaches fall into three broad categories: white-box me…
Pronunciation Editing for Finnish Speech using Phonetic Posteriorgrams
Zirui Li, Lauri Juvela, Mikko Kurimo
Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L2 speech synthesis datasets, it…
Solid State Bus-Comp: A Large-Scale and Diverse Dataset for Dynamic Range Compressor Virtual Analog Modeling
Yicheng Gu, Runsong Zhang, Lauri Juvela +1
Virtual Analog (VA) modeling aims to simulate the behavior of hardware circuits via algorithms to replicate their tone digitally. Dynamic Range Compressor (DRC) is an audio process…
Neurodyne: Neural Pitch Manipulation with Representation Learning and Cycle-Consistency GAN
Yicheng Gu, Chaoren Wang, Zhizheng Wu +1
Pitch manipulation is the process of producers adjusting the pitch of an audio segment to a specific key and intonation, which is essential in music production. Neural-network-base…
Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial approaches
Eloi Moliner, Michal Švento, Alec Wright +3
Accurately estimating nonlinear audio effects without access to paired input-output signals remains a challenging problem. This work studies unsupervised probabilistic approaches f…
Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion
Michal Švento, Eloi Moliner, Lauri Juvela +2
The restoration of nonlinearly distorted audio signals, alongside the identification of the applied memoryless nonlinear operation, is studied. The paper focuses on the difficult b…
Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models
Alec Wright, Alistair Carson, Lauri Juvela
This paper introduces Open-Amp, a synthetic data framework for generating large-scale and diverse audio effects data. Audio effects are relevant to many musical audio processing an…
Audio Codec Augmentation for Robust Collaborative Watermarking of Speech Synthesis
Lauri Juvela, Xin Wang
Automatic detection of synthetic speech is becoming increasingly important as current synthesis methods are both near indistinguishable from human speech and widely accessible to t…
HiFi-Glot: High-Fidelity Neural Formant Synthesis with Differentiable Resonant Filters
Yicheng Gu, Pablo Pérez Zarazaga, Chaoren Wang +4
Formant synthesis aims to generate speech with controllable formant structures, enabling precise control of vocal resonance and phonetic features. However, while existing formant s…
End-to-End Amp Modeling: From Data to Controllable Guitar Amplifier Models
Lauri Juvela, Eero-Pekka Damskägg, Aleksi Peussa +4
This paper describes a data-driven approach to creating real-time neural network models of guitar amplifiers, recreating the amplifiers' sonic response to arbitrary inputs at the f…
DDSP-based Neural Waveform Synthesis of Polyphonic Guitar Performance from String-wise MIDI Input
Nicolas Jonason, Xin Wang, Erica Cooper +3
We explore the use of neural synthesis for acoustic guitar from string-wise MIDI input. We propose four different systems and compare them with both objective metrics and subjectiv…
Collaborative Watermarking for Adversarial Speech Synthesis
Lauri Juvela, Xin Wang
Advances in neural speech synthesis have brought us technology that is not only close to human naturalness, but is also capable of instant voice cloning with little data, and is hi…
Speaker-independent neural formant synthesis
Pablo Pérez Zarazaga, Zofia Malisz, Gustav Eje Henter +1
We describe speaker-independent speech synthesis driven by a small set of phonetically meaningful speech parameters such as formant frequencies. The intention is to leverage deep-l…
Adversarial Guitar Amplifier Modelling With Unpaired Data
Alec Wright, Vesa Välimäki, Lauri Juvela
We propose an audio effects processing framework that learns to emulate a target electric guitar tone from a recording. We train a deep neural network using an adversarial approach…
Conditional Spoken Digit Generation with StyleGAN
Kasperi Palkama, Lauri Juvela, Alexander Ilin
This paper adapts a StyleGAN model for speech generation with minimal or no conditioning on text. StyleGAN is a multi-scale convolutional GAN capable of hierarchically capturing da…
Transferring neural speech waveform synthesizers to musical instrument sounds generation
Yi Zhao, Xin Wang, Lauri Juvela +1
Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the neural-source-filter (NSF) model have shown good performance in speech synthesis despite their different meth…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-spectrogram
Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi +1
Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to m…