Modern deep learning-based models have seen outstanding performance improvement with speech enhancement tasks. The number of parameters of state-of-the-art models, however, is often too large to be deployed on devices for real-world applications. To this end, we propose Tiny Recurrent U-Net (TRU-Net), a lightweight online inference model that matches the performance of current state-of- the-art models. The size of the quantized version of TRU-Net is 362 kilobytes, which is small enough to be deployed on edge devices. In addition, we combine the small-sized model with a new masking method called phase-aware ß-sigmoid mask, which enables simultaneous denoising and dereverberation. Results of both objective and subjective evaluations have shown that our model can achieve competitive performance with the current state-of-the-art models on benchmark datasets using fewer parameters by orders of magnitude.

We present a neural analysis and synthesis (NANSY) framework that can manipulate the voice, pitch, and speed of an arbitrary speech signal. Most of the previous works have focused on using information bottleneck to disentangle analysis features for controllable synthesis, which usually results in poor reconstruction quality. We address this issue by proposing a novel training strategy based on information perturbation. The idea is to perturb information in the original input signal (e.g., formant, pitch, and frequency response), thereby letting synthesis networks selectively take essential attributes to reconstruct the input signal. Because NANSY does not need any bottleneck structures, it enjoys both high reconstruction quality and controllability. Furthermore, NANSY does not require any labels associated with speech data such as text and speaker information, but rather uses a new set of analysis features, i.e., wav2vec feature and newly proposed pitch feature, Yingram, which allows for fully self-supervised training. Taking advantage of fully self-supervised training, NANSY can be easily extended to a multilingual setting by simply training it with a multilingual dataset. The experiments show that NANSY can achieve significant improvement in performance in several applications such as zero-shot voice conversion, pitch shift, and time-scale modification.

This paper presents a novel onset detection algorithm based on cepstral analysis. Instead of considering unnecessary mel-scale or any interests of non-harmonic components, we selectively focus on the changes in particular cepstral coefficients that represent the harmonic structure of an input signal. In comparison with a conventional time-frequency analysis, the advantage of using cepstral coefficients is that it shows the harmonic structure more clearly, and gives a robust detection function even when the envelope of waveform fluctuates or slowly increases. As a detection function, harmonic cepstrum regularity (HCR) is derived by the summation of several harmonic cepstral coefficients, but their frequency indices are defined from the previous frame so as to reflect the temporal changes in the harmonic structure. Experiments show that the proposed algorithm achieves significant improvement in performance over other algorithms, particularly for pitched instruments with soft onsets, such as violin and singing voice.

In this paper, we propose an approach to collaboratively track motion of a moving target in a wide area utilizing camera-equipped visual sensor networks, which are expected to play an essential role in a variety of applications such as surveillance and monitoring. A genetic fitting method for efficient contour extraction is used as inter-scene approach to detect and track the target. We also considered the existence of faulty sensors in the network which deteriorate the difficulty of target tracking problem, and proposed a robust sensor collaboration method. The experimental results have shown that the proposed target tracking approach produces very successful target tracking compared with the existing method especially in case that the target is adjacent to neighboring objects of background