Audio-visual sound source localization (AV-SSL) estimates the position of sound sources by fusing auditory and visual cues. Current AV-SSL methodologies typically require spatially-paired audio-visual data and cannot selectively localize specific target sources. To address these limitations, we introduce Cross-Instance Audio-Visual Localization (CI-AVL), a novel task that localizes target sound sources using visual prompts from different instances of the same semantic class. CI-AVL enables selective localization without spatially paired data. To solve this task, we propose AV-SSAN, a semantic-spatial alignment framework centered on a Multi-Band Semantic-Spatial Alignment Network (MB-SSA Net). MB-SSA Net decomposes the audio spectrogram into multiple frequency bands, aligns each band with semantic visual prompts, and refines spatial cues to estimate the direction-of-arrival (DoA). To facilitate this research, we construct VGGSound-SSL, a large-scale dataset comprising 13,981 spatial audio clips across 296 categories, each paired with visual prompts. AV-SSAN achieves a mean absolute error of 16.59° and an accuracy of 71.29%, significantly outperforming existing AV-SSL methods.

The prevailing noise-resistant and reverberation-resistant localization algorithms primarily emphasize separating and providing directional output for each speaker in multi-speaker scenarios, without association with the identity of speakers. In this paper, we present a target speaker localization algorithm with a selective hearing mechanism. Given a reference speech of the target speaker, we first produce a speaker-dependent spectrogram mask to eliminate interfering speakers’ speech. Subsequently, a Long-Short-Term Memory (LSTM) network is employed to extract the target speaker’s location from the filtered spectrogram. Experiments validate the superiority of our proposed method over existing algorithms for different scale-invariant signal-to-noise ratios (SNR) conditions. Specifically, at SNR = -10 dB, our proposed network LocSelect achieves a mean absolute error (MAE) of 3.55° and an accuracy (ACC) of 87.40%.

Speaker tracking plays a significant role in numerous real-world human robot interaction (HRI) applications. In recent years, there has been a growing interest in utilizing multi-sensory information, such as complementary audio and visual signals, to address the challenges of speaker tracking. Despite the promising results, existing approaches still encounter difficulties in accurately determining the speaker’s true location, particularly in adverse conditions such as speech pauses, reverberation, or visual occlusions, leading to missed detections or spurious estimates. In this paper, we propose a novel speaker tracking method based on the Generalized Labelled Multi-Bernoulli (GLMB) filter. Our method operates in 3D space using audio information captured by a microphone array and video streams obtained from a monocular camera. The GLMB-based tracker effectively handles outliers in location estimates and maintains tracking during periods of missed detections. Experiments conducted on the publicly available AV16.3 dataset show that our proposal surpasses other competitive methods with improved results.

On the emerging development of SiC MOSFET, the switching frequency of power converters has been pushed to the order of hundreds of kilohertz or even megahertz to improve power density. However, the state-of-the-art simulations for such high-frequency power electronics circuits are usually time-consuming, blocking the studies of long dynamics such as several seconds. What’s worse, adopting physical models to simulate the turn-on and turn-off transients leads to high stiffness and therefore convergence problems. In this paper, an event-driven approach is proposed to improve the solving speed and simulation stability of SiC based high-frequency converters. A time-scale hierarchical automaton is proposed to model the system, where piecewise analytical transient model for SiC MOSFET is established as the second layer in the automaton to simulate the switching transients with improved numerical stability. Simulation of the system is driven by discrete events in the automaton. A 3.3 kW 79.1 kHz wireless power transfer system for the wireless charging of electric vehicle is studied. Compared with ideal switch model in Simulink and physical switch model in LTspice, 60-fold and 400-fold increases in terms of simulation speed with same level of accuracy are achieved respectively. The simulated results are also attested to by experimental verifications.

The interaction between the resonant wireless power transfer (WPT) component and the dc/dc converter may weaken the system stability, causing overshoot and oscillation in the dynamic process of such WPT system. Nevertheless, both the mechanism and the solutions of the above problems are not fully researched for the WPT systems. This paper first analyzes the small-signal impedance characteristics of such systems and points out the cause of the instability phenomenon. Then, a novel control strategy is proposed to regulate the system impedance characteristics, which can suppress the interaction and improve the system dynamic performance. Small-signal analysis and experiment results demonstrate that the proposed strategy has the advantages of fast response and less overshoot compared with traditional PI control, and is suitable for different operating conditions.

Most of multichannel sound source Direction Of Arrival (DOA) estimation algorithms suffer from spatial aliasing problems. The phase differences between a pair of microphones are wrapped beyond the spatial aliasing frequency. A common solution is to adjust the distance between the microphones to obtain a suitable aliasing frequency, and take only the frequency band below the aliasing frequency for localization. With correct phase unwrapping, a broader frequency band can be utilized for localization. In this paper, we investigate a method for phase unwrapping solving the spatial aliasing problem for scenarios with a single source and high-level diffuse background noise (around 0dB SNR). The aliasing frequency is estimated from the signal, and is used to unwrap a phase difference vector. Pre- and post-processing steps are applied to increase the robustness. Our experiments with a large number of simulated and real signals demonstrate the robustness of our method in noise.

Noise reduction technologies have been applied to enhance the intelligibility of voice communications. However, existing methods are vulnerable to complex non-stationary noisy conditions, which are commonly encountered in real world hands-free scenarios. Additionally, the existing methods do not fully take the advantage of the deployment of multi-channel microphone arrays on the burgeoning high-end mobile phones. In this paper, we propose a noise estimation method based on a hybrid of efficient single channel and globally optimized multi-channel microphone noise estimation using adaptive noise coherence models. Evaluation results on real-world recordings collected via a smartphone confirm its superior effectiveness to suppress the fast-varying noises compared to the state-of-the-art baseline methods.

Methods are available for simultaneous localization of multiple (unknown) audio sources using microphone arrays. Typical algorithms aim at localizing all active sources. They moreover require that the number of sources is known and is less than or equal the number of microphones. This constraint cannot be satisfied in many reallife situations and noisy environments. We present an algorithm for localizing an audio source with known statistics in a multi-source environment. The proposed method circumvents the mentioned problems by using a phase-preserving signal extraction method on the input signal. A binary mask is estimated and used to retain only the information of the target source in the original microphone signals. The masked signals are fed to a modified version of a conventional localization algorithm, which now localizes only the target source. Experimental results obtained from real recordings show that the proposed method can successfully detect and localize the target source.