Ambient sound scenes typically comprise multiple short events occurring on top of a somewhat stationary background. We consider the task of separating these events from the background, which we call foreground-background ambient sound scene separation. We propose a deep learning-based separation framework with a suitable feature normaliza-tion scheme and an optional auxiliary network capturing the background statistics, and we investigate its ability to handle the great variety of sound classes encountered in ambient sound scenes, which have often not been seen in training. To do so, we create single-channel foreground-background mixtures using isolated sounds from the DESED and Audioset datasets, and we conduct extensive experiments with mixtures of seen or unseen sound classes at various signal-to-noise ratios. Our experimental findings demonstrate the generalization ability of the proposed approach.
From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene. We ask whether these ambient sounds convey information about 3D scene structure and, if so, whether they provide a useful learning signal for multimodal models. To study this, we collect a dataset of paired audio and RGB-D recordings from a variety of quiet indoor scenes. We then train models that estimate the distance to nearby walls, given only audio as input. We also use these recordings to learn multimodal representations through self-supervision, by training a network to associate…
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；International Audio Laboratories Erlangen（埃尔朗根国际音频实验室）；Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；WS Audiology（WS听力集团）
A geometrically-motivated method for primary-ambient decomposition is proposed and evaluated in an up-mixing application. The method consists of two steps, accommodating a particularly intuitive explanation. The first step consists of signal-adaptive rotations applied on the input stereo scene, which translate the primary sound sources into the center of the rotated scene. The second step applies a center-channel extraction method, based on a simple signal model and optimal in the mean-squared-error sense. The performance is evaluated by using the estimated ambient component to enable surround…
Tampere University（坦佩雷大学）；Universite de Lorraine（洛林大学）
This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD). The dataset is based on emulation of real recordings of static or moving sound events under real conditions of reverberation and ambient noise, using spatial room impulse responses captured in a variety of rooms and delivered in two spatial formats. The acoustical synthesis remains the same as in the previous iteration of the challenge, however the new dataset brings more challenging conditions of polyphony and overlapping instances of the same class. The most im…
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms
Music and Audio Research Laboratory, New York University（纽约大学音乐与音频研究实验室）；Viterbi School of Engineering, University of Southern California（南加州大学维特比工程学院）
Institute of Artificial Intelligence, Philipps-University Marburg（马尔堡大学人工智能研究所）；Clinic of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel（巴塞尔大学医院口腔颌面外科诊所）；Division of Respiratory and Critical Care Medicine, Philipps-University, Marburg（马尔堡大学呼吸与重症医学科）；Division of Internal Medicine, University Hospital Basel（巴塞尔大学医院内科）；Department of Gastroenterology, Endocrinology, Metabolism and Infectiology, Philipps-University, Marburg（马尔堡大学消化内科、内分泌科、代谢与感染病科）；Cardiology Department, University Hospital Gießen and Marburg（吉森和马尔堡大学医院心脏病科）；Department of Cardiology, University Heart and Vascular Center Hamburg（汉堡大学心脏和血管中心心脏病科）
Objective: This work proposes a semi-supervised training approach for detecting lung and heart sounds simultaneously with only one trained model and in invariance to the auscultation point. Methods: We use open-access data from the 2016 Physionet/CinC Challenge, the 2022 George Moody Challenge, and from the lung sound database HF_V1. We first train specialist single-task models using foreground ground truth (GT) labels from different auscultation databases to identify background sound events in the respective lung and heart auscultation databases. The pseudo-labels generated in this way were c…
The performance of sound event detection methods can significantly degrade when they are used in unseen conditions (e.g. recording devices, ambient noise). Domain adaptation is a promising way to tackle this problem. In this paper, we present VOICe, the first dataset for the development and evaluation of domain adaptation methods for sound event detection. VOICe consists of mixtures with three different sound events ("baby crying", "glass breaking", and "gunshot"), which are over-imposed over three different categories of acoustic scenes: vehicle, outdoors, and indoors. Moreover, the mixtures …
This report presents the dataset and the evaluation setup of the Sound Event Localization & Detection (SELD) task for the DCASE 2020 Challenge. The SELD task refers to the problem of trying to simultaneously classify a known set of sound event classes, detect their temporal activations, and estimate their spatial directions or locations while they are active. To train and test SELD systems, datasets of diverse sound events occurring under realistic acoustic conditions are needed. Compared to the previous challenge, a significantly more complex dataset was created for DCASE 2020. The two key di…
Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Loria（洛林计算机科学及其应用实验室）；Johannes Kepler University of Linz（林茨约翰内斯·开普勒大学）；Carnegie Mellon University（卡内基梅隆大学）
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mainly focused on separating speech from noise, speech from speech, musical instruments from each other, or sound events from each other. However, separating an audio mixture (e.g., movie soundtrack) into the three broad categories of speech, music, and sound effects (understood to include ambient noise and natural sound events) has been left largely unexplored, despite a wide range of potential applications. This paper…
National Taiwan University（台湾大学）；ASUS Intelligent Cloud Services（华硕智能云服务）
Human perceives rich auditory experience with distinct sound heard by ears. Videos recorded with binaural audio particular simulate how human receives ambient sound. However, a large number of videos are with monaural audio only, which would degrade the user experience due to the lack of ambient information. To address this issue, we propose an audio spatialization framework to convert a monaural video into a binaural one exploiting the relationship across audio and visual components. By preserving the left-right consistency in both audio and visual modalities, our learning strategy can be vie…
Cornell Lab of Ornithology, Cornell University（康奈尔大学康奈尔鸟类学实验室）；Music and Audio Research Laboratory, New York University（纽约大学音乐与音频研究实验室）；Center for Urban Science and Progress, New York University（纽约大学城市科学与进步中心）
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop,…
