Document detail
ID
oai:arXiv.org:2406.08800
Topic
Computer Science - Sound Computer Science - Machine Learnin... Electrical Engineering and Systems...Author
Feng, Tiantian Dimitriadis, Dimitrios Narayanan, ShrikanthCategory
Computer Science
Year
2024
listing date
9/4/2024
Keywords
science recognition modeling models audioAbstract
Recent advances in foundation models have enabled audio-generative models that produce high-fidelity sounds associated with music, events, and human actions.
Despite the success achieved in modern audio-generative models, the conventional approach to assessing the quality of the audio generation relies heavily on distance metrics like Frechet Audio Distance.
In contrast, we aim to evaluate the quality of audio generation by examining the effectiveness of using them as training data.
Specifically, we conduct studies to explore the use of synthetic audio for audio recognition.
Moreover, we investigate whether synthetic audio can serve as a resource for data augmentation in speech-related modeling.
Our comprehensive experiments demonstrate the potential of using synthetic audio for audio recognition and speech-related modeling.
Our code is available at https://github.com/usc-sail/SynthAudio.
;Comment: Accepted to 2024 INTERSPEECH; corrections to ActivityNet labels
Feng, Tiantian,Dimitriadis, Dimitrios,Narayanan, Shrikanth, 2024, Can Synthetic Audio From Generative Foundation Models Assist Audio Recognition and Speech Modeling?
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