Representational learning for an anomalous sound detection system with source separation model

The detection of anomalous sounds in machinery operation presents a significant challenge due to the difficulty in generalizing anomalous acoustic patterns. This task is typically approached as an unsupervised learning or novelty detection problem, given the complexities associated with the acquisition of comprehensive anomalous acoustic data. Conventional methodologies for training anomalous sound detection systems primarily employ auto-encoder architectures or representational learning with auxiliary tasks. However, both approaches have inherent limitations. Auto-encoder structures are constrained to utilizing only the target machine's operational sounds, while training with auxiliary tasks, although capable of incorporating diverse acoustic inputs, may yield representations that lack correlation with the characteristic acoustic signatures of anomalous conditions. We propose a training method based on the source separation model (CMGAN) that aims to isolate non-target machine sounds from a mixture of target and non-target class acoustic signals. This approach enables the effective utilization of diverse machine sounds and facilitates the training of complex neural network architectures with limited sample sizes. Our experimental results demonstrate that the proposed method yields better performance compared to both conventional auto-encoder training approaches and source separation techniques that focus on isolating target machine signals. Moreover, our experimental results demonstrate that the proposed method exhibits the potential for enhanced representation learning as the quantity of non-target data increases, even while maintaining a constant volume of target class data.

Conditional variational autoencoder to improve neural audio synthesis for polyphonic music sound

Deep generative models for audio synthesis have recently been significantly improved. However, the task of modeling raw-waveforms remains a difficult problem, especially for audio waveforms and music signals. Recently, the realtime audio variational autoencoder (RAVE) method was developed for high-quality audio waveform synthesis. The RAVE method is based on the variational autoencoder and utilizes the two-stage training strategy. Unfortunately, the RAVE model is limited in reproducing wide-pitch polyphonic music sound. Therefore, to enhance the reconstruction performance, we adopt the pitch activation data as an auxiliary information to the RAVE model. To handle the auxiliary information, we propose an enhanced RAVE model with a conditional variational autoencoder structure and an additional fully-connected layer. To evaluate the proposed structure, we conducted a listening experiment based on multiple stimulus tests with hidden references and an anchor (MUSHRA) with the MAESTRO. The obtained results indicate that the proposed model exhibits a more significant performance and stability improvement than the conventional RAVE model.