12 accepted papers
In this paper, we introduce SoloAudio, a novel diffusion-based generative model for target sound extraction (TSE). Our approach trains latent diffusion models on audio, replacing the previous U-Net backbone with a skip-connected Transformer that operates on latent features. SoloAudio supports both a…
SONAR technology plays a pivotal role in terrain exploration and specifically identification of objects of interest. However, it grapples with a recurring challenge of clutter and noise which limits the performance of target recognition models. The challenge of noisy observations renders the choice…
Common target sound extraction (TSE) approaches primarily relied on discriminative approaches in order to separate the target sound while minimizing interference from the unwanted sources, with varying success in separating the target from the background. This study introduces DPM-TSE, a generative…
Auditory Attention Decoding (AAD) algorithms play a crucial role in isolating desired sound sources within challenging acoustic environments directly from brain activity. Although recent research has shown promise in AAD using shallow representations such as auditory envelope and spectrogram, there…
Temporal coherence is a feature-binding mechanism that ensures features that evolve together in time belong to the same object or event. Coherence has been extensively studied in biological systems, demonstrating how our brain leverages this mechanism to perform complex tasks in real environments an…
Sound Event Detection (SED) tackles the challenge of identifying sound events in an audio recording by delimiting both their temporal boundaries as well as sound category. With recent advances in deep learning, current systems are able to leverage availability of large datasets to train sophisticate…
Sound event detection (SED) takes on the task of identifying presence of specific sound events in a complex audio recording. SED has tremendous implications in video analytics, smart speaker algorithms and audio tagging. Recent advances in deep learning have afforded remarkable advances in performan…
Attention plays a vital role in helping us navigate our acoustic surroundings. It guides sensory processing to sift through the cacophony of sounds in everyday scenes and modulates the representation of targets sounds relative to distractors. While its conceptual role is well established, there are…
Synthesis of music content generally leverages the underlying statistical structure of music to develop generative models, able to create new musical expressions within the same genre. In this work, we explore the statistical structure of a musical corpus and its effect on modulating the attention o…
Sound event detection is a challenging task, especially for scenes with multiple simultaneous events. While event classification methods tend to be fairly accurate, event localization presents additional challenges, especially when large amounts of labeled data are not available. Task4 of the 2018 D…
Demands on auditory perception change constantly with natural changes in everyday acoustic environments. Mechanisms, such as attentional feedback, direct the brain to adapt processing of the incoming signal to maximize its ability to detect the presence of a sound of interest or enhance its represen…
Detection of anomalous sound events in audio surveillance is a challenging task when applied to realistic settings. Part of the difficulty stems from properly defining the `normal' behavior of a crowd or an environment (e.g. airport, train station, sport field). By successfully capturing the heterog…