Audio signal processing systems often operate differently depending on the recording devices, leading to performance discrepancies. Therefore, it is important to know about the characteristics of the recording device; however, it is difficult to know the device’s behavior in most cases. In this study, we propose a diffusion-model-based device characteristic transfer to estimate the device’s frequency response only with the recorded signals. By joint-training the conditional and unconditional diffusion models, it is found that non-linear distortions and some filtered signals are reflected more than by only training the conditional model. We show that the proposed method transfers the style closely to the ground truth not only visually on the spectrogram but also the t-distributed stochastic neighbor embedding distribution and the performance of the device classifier. We also show the proposed method enhancing the performance as a data augmentation method for acoustic scene classification.

Score-based generative models have shown the real-like quality of synthesized speech in the text-to-speech (TTS) area. However, the critical artifact of score-based models is the requirement of a high computational cost due to the iterative sampling algorithm, and it also makes it difficult to fine-tune the score-based TTS-optimized vocoder. In this study, we propose a method of joint training the score-based TTS model and HiFi-GAN using the compressed log-mel features, and it guarantees a significant speech quality even on the non-iterative sampling. As a result, the proposed method overcomes some digital artifacts of the synthesized audios compared to the non-iterative sampling of Grad-TTS. Also, the non-iterative sampling can generate speech faster than other end-to-end TTS models with fewer parameters.