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Found 12 papers, 1 papers with code
Example-Based Framework for Perceptually Guided Audio Texture Generation
By using a few synthetic examples to indicate the presence or absence of a semantic attribute, we infer the guidance vectors in the latent space of the StyleGAN to control that attribute during generation.
VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research
Researchers have used machine learning approaches to identify motion sickness in VR experience.
Towards Controllable Audio Texture Morphing
In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes.
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures
These metrics use deep features that summarize the statistics of any given audio texture, thus being inherently sensitive to variations in the statistical parameters that define an audio texture.
PoLyScriber: Integrated Fine-tuning of Extractor and Lyrics Transcriber for Polyphonic Music
Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility.
Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling
We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone.
Music-robust Automatic Lyrics Transcription of Polyphonic Music
To improve the robustness of lyrics transcription to the background music, we propose a strategy of combining the features that emphasize the singing vocals, i. e. music-removed features that represent singing vocal extracted features, and the features that capture the singing vocals as well as the background music, i. e. music-present features.
Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music
Lyrics transcription of polyphonic music is challenging not only because the singing vocals are corrupted by the background music, but also because the background music and the singing style vary across music genres, such as pop, metal, and hip hop, which affects lyrics intelligibility of the song in different ways.
An Integrated System Architecture for Generative Audio Modeling
We introduce a new system for data-driven audio sound model design built around two different neural network architectures, a Generative Adversarial Network(GAN) and a Recurrent Neural Network (RNN), that takes advantage of the unique characteristics of each to achieve the system objectives that neither is capable of addressing alone.
Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks
Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation consisting of log magnitude and instantaneous frequency (the "IFSpectrogram").
Automatic Lyrics Alignment and Transcription in Polyphonic Music: Does Background Music Help?
Automatic lyrics alignment and transcription in polyphonic music are challenging tasks because the singing vocals are corrupted by the background music.
Audio and Speech Processing Sound
Acoustic Modeling for Automatic Lyrics-to-Audio Alignment
In this work, we propose (1) using additional speech and music-informed features and (2) adapting the acoustic models trained on a large amount of solo singing vocals towards polyphonic music using a small amount of in-domain data.
