Single-channel audio separation aims to separate individual sources from a single-channel mixture. Most existing methods rely on supervised learning with synthetically generated paired data. However, obtaining high-quality paired data in real-world scenarios is often difficult. This data scarcity can degrade model performance under unseen conditions and limit generalization ability. To this end, in this work, we approach this problem from an unsupervised perspective, framing it as a probabilistic inverse problem. Our method requires only diffusion priors trained on individual sources. Separation is then achieved by iteratively guiding an initial state toward the solution through reconstruction guidance. Importantly, we introduce an advanced inverse problem solver specifically designed for separation, which mitigates gradient conflicts caused by interference between the diffusion prior and reconstruction guidance during inverse denoising. This design ensures high-quality and balanced separation performance across individual sources. Additionally, we find that initializing the denoising process with an augmented mixture instead of pure Gaussian noise provides an informative starting point that significantly improves the final performance. To further enhance audio prior modeling, we design a novel time–frequency attention-based network architecture that demonstrates strong audio modeling capability. Collectively, these improvements lead to significant performance gains, as validated across speech–sound event, sound event, and speech separation tasks.

This paper aims to achieve single-channel target speech extraction (TSE) in enclosures by solely utilizing distance information. This is the first work that utilizes only distance cues without using speaker physiological information for single-channel TSE. Inspired by recent single-channel Distance-based separation and extraction methods, we introduce a novel model that efficiently fuses distance information with time-frequency (TF) bins for TSE. Experimental results in both single-room and multi-room scenarios demonstrate the feasibility and effectiveness of our approach. This method can also be employed to estimate the distances of different speakers in mixed speech. Online demos are available at https://runwushi.github.io/distance-demo-page/.

Search and rescue (SAR) operations in large-scale disaster sites, such as areas affected by earthquakes, require rapid victim detection. While drones equipped with cameras are commonly used for SAR, their effectiveness is limited in visually obstructed environments, because of debris, smoke, or fog. Under such situations, auditory information can play a crucial role in locating victims who are not visible. Existing drone audition research has demonstrated the feasibility of detecting sound sources using onboard microphone arrays. However, most studies focus on single-drone systems, which face limitations in coverage and accessibility, particularly in complex environments such as collapsed buildings or urban canyons. Additionally, real-world validation of multi-drone audition systems remains limited, with prior studies relying primarily on simulations or controlled environments. To address these challenges, we propose and evaluate a Multi-Drone and Robot-Based Active Audition System (SAAS-RD: Swarm Active Audition System with Robots and Drones) that integrates multiple drones and ground robots to enhance acoustic search capabilities. Our work focuses on real-world performance validation, conducting field experiments in outdoor environments and analyzing system feasibility through case studies. The results demonstrate the potential of SAAS-RD as a practical solution for large-scale SAR operations.

Accurately estimating sound source positions is crucial for robot audition. However, existing sound source localization methods typically rely on a microphone array with at least two spatially preconfigured microphones. This requirement hinders the applicability of microphone-based robot audition systems and technologies. To alleviate these challenges, we propose an online sound source localization method that uses a single microphone mounted on a mobile robot in reverberant environments. Specifically, we develop a lightweight neural network model with only 43k parameters to perform real-time distance estimation by extracting temporal information from reverberant signals. The estimated distances are then processed using an extended Kalman filter to achieve online sound source localization. To the best of our knowledge, this is the first work to achieve online sound source localization using a single microphone on a moving robot, a gap that we aim to fill in this work. Extensive experiments demonstrate the effectiveness and merits of our approach. To benefit the broader research community, we have open-sourced our code at https://github.com/JiangWAV/single-mic-SSL.

A noise covariance matrix (NCM) estimation method for unmanned aerial vehicle (UAV) audition is proposed with rotor noise reduction as its primary focus. The proposed NCM estimation method could be incorporated into audio processing algorithms using UAV-mounted microphone array systems. The NCM is formed through accurate estimation of the microphone array input signal's amplitude and phase by using a multi-sensory rotor noise power spectral density (PSD) estimator and a filter formed by exploiting the acoustical relationship between the microphone array and the rotor noise sources, respectively. The estimated NCM aims to be readily incorporable into several source enhancement algorithms to reduce the effects of rotor noise and improve the resultant audio quality. Experiment evaluation using real in-flight UAV rotor noise recordings shows that the estimated NCM significantly improves rotor noise reduction (of up to$\sim 28$dB) and the quality of the target sound.

The field of bioacoustics is concerned with monitoring wild animals based on their vocalisations. Passive acoustic recorders are now commonly used to collect data of the soundscapes of our wild places. While the data they collect is extremely useful, the majority of the recorders use a single omnidirectional microphone, and thus cannot independently perform localisation of a calling animal. Localisation can be useful to differentiate between multiple calling animals, to improve statistical estimates of abundance, and to locate calling posts, which may be close to nests. In this paper, we consider the design of a low-cost, practical, passive directional acoustic recorder that will facilitate animal localisation, and present and evaluate a prototype system for this purpose.