Towards the Synthesis of Non-speech Vocalizations
This work addresses a domain-specific problem in audio synthesis for non-speech vocalizations, but it is incremental as it applies an existing method to new data.
The paper tackled unconditional generation of infant cry sounds using the DiffWave framework, achieving high fidelity and diversity in the synthesized audio.
In this report, we focus on the unconditional generation of infant cry sounds using the DiffWave framework, which has shown great promise in generating high-quality audio from noise. We use two distinct datasets of infant cries: the Baby Chillanto and the deBarbaro cry dataset. These datasets are used to train the DiffWave model to generate new cry sounds that maintain high fidelity and diversity. The focus here is on DiffWave's capability to handle the unconditional generation task.