5 citations · 16 across the 12 of their papers we have counts for
12 papers
MorphFader: Enabling Fine-grained Controllable Morphing with Text-to-Audio Models
Purnima Kamath, Chitralekha Gupta, Suranga Nanayakkara
Sound morphing is the process of gradually and smoothly transforming one sound into another to generate novel and perceptually hybrid sounds that simultaneously resemble both. Rece…
Example-Based Framework for Perceptually Guided Audio Texture Generation
Purnima Kamath, Chitralekha Gupta, Lonce Wyse +1
Controllable generation using StyleGANs is usually achieved by training the model using labeled data. For audio textures, however, there is currently a lack of large semantically l…
VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research
Elliott Wen, Chitralekha Gupta, Prasanth Sasikumar +5
Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches demand an accurately-labeled, real-world, and diverse dataset for h…
Towards Controllable Audio Texture Morphing
Chitralekha Gupta, Purnima Kamath, Yize Wei +3
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 class…
Parameter Sensitivity of Deep-Feature based Evaluation Metrics for Audio Textures
Chitralekha Gupta, Yize Wei, Zequn Gong +3
Standard evaluation metrics such as the Inception score and Fréchet Audio Distance provide a general audio quality distance metric between the synthesized audio and reference clean…
PoLyScriber: Integrated Fine-tuning of Extractor and Lyrics Transcriber for Polyphonic Music
Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
Lyrics transcription of polyphonic music is challenging as the background music affects lyrics intelligibility. Typically, lyrics transcription can be performed by a two-step pipel…
Sound Model Factory: An Integrated System Architecture for Generative Audio Modelling
Lonce Wyse, Purnima Kamath, Chitralekha Gupta
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 Ne…
Music-robust Automatic Lyrics Transcription of Polyphonic Music
Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
Lyrics transcription of polyphonic music is challenging because singing vocals are corrupted by the background music. To improve the robustness of lyrics transcription to the backg…
Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music
Xiaoxue Gao, Chitralekha Gupta, Haizhou Li
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 sin…
Signal Representations for Synthesizing Audio Textures with Generative Adversarial Networks
Chitralekha Gupta, Purnima Kamath, Lonce Wyse
Generative Adversarial Networks (GANs) currently achieve the state-of-the-art sound synthesis quality for pitched musical instruments using a 2-channel spectrogram representation c…
Automatic Lyrics Alignment and Transcription in Polyphonic Music: Does Background Music Help?
Chitralekha Gupta, Emre Yılmaz, Haizhou Li
Background music affects lyrics intelligibility of singing vocals in a music piece. Automatic lyrics alignment and transcription in polyphonic music are challenging tasks because t…
Acoustic Modeling for Automatic Lyrics-to-Audio Alignment
Chitralekha Gupta, Emre Yılmaz, Haizhou Li
Automatic lyrics to polyphonic audio alignment is a challenging task not only because the vocals are corrupted by background music, but also there is a lack of annotated polyphonic…