Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；Western Washington University（西华盛顿大学）；DocuSign（DocuSign公司）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Piper Wolters, Logan Sizemore, Chris Daw, Brian Hutchinson, Lauren Phillips
备注：
Updated results based on additional experimentation and moved dataset generation prose to stand-alone section
Many applications involve detecting and localizing specific sound events within long, untrimmed documents, including keyword spotting, medical observation, and bioacoustic monitoring for conservation. Deep learning techniques often set the state-of-the-art for these tasks. However, for some types of events, there is insufficient labeled data to train such models. In this paper, we propose a region proposal-based approach to few-shot sound event detection utilizing the Perceiver architecture. Motivated by a lack of suitable benchmark datasets, we generate two new few-shot sound event localization datasets: "Vox-CASE," using clips of celebrity speech as the sound event, and "ESC-CASE," using environmental sound events. Our highest performing proposed few-shot approaches achieve 0.483 and 0.418 F1-score, respectively, with 5-shot 5-way tasks on these two datasets. These represent relative improvements of 72.5% and 11.2% over strong proposal-free few-shot sound event detection baselines.
Ritsumeikan University（立命馆大学）；LINE Corporation（LINE公司）；The University of Tokyo（东京大学）
Synthesizing and converting environmental sounds have the potential for many applications such as supporting movie and game production, data augmentation for sound event detection and scene classification. Conventional works on synthesizing and converting environmental sounds are based on a physical modeling or concatenative approach. However, there are a limited number of works that have addressed environmental sound synthesis and conversion with statistical generative models; thus, this research area is not yet well organized. In this paper, we review problem definitions, applications, and e…
Ritsumeikan University（立命馆大学）；Doshisha University（同志社大学）；The University of Tokyo（东京大学）；Kansai University（关西大学）
Environmental sound synthesis is a technique for generating a natural environmental sound. Conventional work on environmental sound synthesis using sound event labels cannot finely control synthesized sounds, for example, the pitch and timbre. We consider that onomatopoeic words can be used for environmental sound synthesis. Onomatopoeic words are effective for explaining the feature of sounds. We believe that using onomatopoeic words will enable us to control the fine time-frequency structure of synthesized sounds. However, there is no dataset available for environmental sound synthesis using…
Ritsumeikan University（立命馆大学）；Doshisha University（同志社大学）；The University of Tokyo（东京大学）；Kansai University（关西大学）
In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words. As one way of expressing an environmental sound, we can use an onomatopoeic word, which is a character sequence for phonetically imitating a sound. An onomatopoeic word is effective for describing diverse sound features. Therefore, using onomatopoeic words for environmental sound synthesis will enable us to generate diverse environmental sounds. To generate diverse sounds, we propose a method based on a sequence-to-sequence framework for synthesizing environmental sounds from onomatopoeic words. We…
National Institute of Technology, Tokuyama College（德山国立技术学院）；The University of Tokyo（东京大学）；Doshisha University（同志社大学）；Ritsumeikan University（立命馆大学）
We propose a method for synthesizing environmental sounds from visually represented onomatopoeias and sound sources. An onomatopoeia is a word that imitates a sound structure, i.e., the text representation of sound. From this perspective, onoma-to-wave has been proposed to synthesize environmental sounds from the desired onomatopoeia texts. Onomatopoeias have another representation: visual-text representations of sounds in comics, advertisements, and virtual reality. A visual onomatopoeia (visual text of onomatopoeia) contains rich information that is not present in the text, such as a long-sh…
Ritsumeikan University（立命馆大学）；Doshisha University（同志社大学）；The University of Tokyo（东京大学）
Although several methods of environmental sound synthesis have been proposed, there has been no discussion on how synthesized environmental sounds should be evaluated. Only either subjective or objective evaluations have been conducted in conventional evaluations, and it is not clear what type of evaluation should be carried out. In this paper, we investigate how to evaluate synthesized environmental sounds. We also propose a subjective evaluation methodology to evaluate whether the synthesized sound appropriately represents the information input to the environmental sound synthesis system. In…
College of Engineering, Trivandrum（特里凡得琅工程学院）；Govt. Engineering College, Barton Hill, Thiruvananthapuram（巴顿山政府工程学院）
The research in Environmental Sound Classification (ESC) has been progressively growing with the emergence of deep learning algorithms. However, data scarcity poses a major hurdle for any huge advance in this domain. Data augmentation offers an excellent solution to this problem. While Generative Adversarial Networks (GANs) have been successful in generating synthetic speech and sounds of musical instruments, they have hardly been applied to the generation of environmental sounds. This paper presents EnvGAN, the first ever application of GANs for the adversarial generation of environmental sou…
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We propose an environmental-sound-extraction method using onomatopoeic words to specify the target sound to be extracted. By this method, we estimate a time-frequency mask from an input mixture spectrogram and an onomatopoeic word using a U-Net architecture, then extract the corresponding target sound by masking the spectrogram. Experimental results indicate that the proposed method can extract only the target sound cor…
Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The classification performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose a convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, w…
Environmental sound classification systems often do not perform robustly across different sound classification tasks and audio signals of varying temporal structures. We introduce a multi-stream convolutional neural network with temporal attention that addresses these problems. The network relies on three input streams consisting of raw audio and spectral features and utilizes a temporal attention function computed from energy changes over time. Training and classification utilizes decision fusion and data augmentation techniques that incorporate uncertainty. We evaluate this network on three …
Sapporo city university（札幌市立大学）；Hokkaido University（北海道大学）
This paper proposes new acoustic feature signatures based on the multiscale fractal dimension (MFD), which are robust against the diversity of environmental sounds, for the content-based similarity search. The diversity of sound sources and acoustic compositions is a typical feature of environmental sounds. Several acoustic features have been proposed for environmental sounds. Among them is the widely-used Mel-Frequency Cepstral Coefficients (MFCCs), which describes frequency-domain features. However, in addition to these features in the frequency domain, environmental sounds have other import…
Shanghai Institute for Advanced Communication and Data Science, Shanghai University（上海大学上海先进通信与数据科学研究院）
Environmental sound classification (ESC) is an important and challenging problem. In contrast to speech, sound events have noise-like nature and may be produced by a wide variety of sources. In this paper, we propose to use a novel deep convolutional neural network for ESC tasks. Our network architecture uses stacked convolutional and pooling layers to extract high-level feature representations from spectrogram-like features. Furthermore, we apply mixup to ESC tasks and explore its impacts on classification performance and feature distribution. Experiments were conducted on UrbanSound8K, ESC-5…
Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；University of Surrey（萨里大学）
Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, temporal attention mechanisms have been used in CNN to capture the useful information from the relevant time frames for audio classification, especially for weakly labelled data where the onset and offset times of the sound events are not applied. In these methods, however, the inherent spectral characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a novel parallel temporal-spect…
This paper introduces the Procedural (audio) Variational autoEncoder (ProVE) framework as a general approach to learning Procedural Audio PA models of environmental sounds with an improvement to the realism of the synthesis while maintaining provision of control over the generated sound through adjustable parameters. The framework comprises two stages: (i) Audio Class Representation, in which a latent representation space is defined by training an audio autoencoder, and (ii) Control Mapping, in which a joint function of static/temporal control variables derived from the audio and a random samp…
Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；Shanghai University（上海大学）
Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. However, ESC often suffers from the semantically irrelevant frames and silent frames. In order to deal with this, we employ a frame-level attention model to focus on the semantically relevant frames and salient frames. Specifically, we first propose an convolutional recurrent neural network to learn spectro-temporal features and temporal correlations. Then, we extend o…
Shanghai Institute for Advanced Communication and Data Science（上海先进通信与数据科学研究院）；Shanghai University（上海大学）
Environmental Sound Classification (ESC) is an important and challenging problem, and feature representation is a critical and even decisive factor in ESC. Feature representation ability directly affects the accuracy of sound classification. Therefore, the ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds. In this paper, we propose a subspectrogram segmentation based ESC classification framework. In addition, we adopt the proposed Convolutional Recurrent Neural Network (CRNN) and score level fusion to jointly improve th…
Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-bas…
National University of Defense Technology（国防科技大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
Motivated by the fact that characteristics of different sound classes are highly diverse in different temporal scales and hierarchical levels, a novel deep convolutional neural network (CNN) architecture is proposed for the environmental sound classification task. This network architecture takes raw waveforms as input, and a set of separated parallel CNNs are utilized with different convolutional filter sizes and strides, in order to learn feature representations with multi-temporal resolutions. On the other hand, the proposed architecture also aggregates hierarchical features from multi-level…
AFT: An Exemplar-Free Class Incremental Learning Method for Environmental Sound Classification
South China University of Technology, China（南方科技大学）；The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））；The University of Melbourne, Australia（墨尔本大学）
Spike Encoding for Environmental Sound: A Comparative Benchmark
Audio and Neuromorphic Processing Group, Instituto Tecnologico de Informatica (ITI), Valencia, Spain（音频与神经形态处理组，技术信息研究所（ITI），西班牙瓦伦西亚）；Department of Computer Science, Universitat de Valencia, 46100 Burjassot, Spain（计算机科学系，瓦伦西亚大学，西班牙布尔贾索特）
Environmental Sound Classification (ESC) is a challenging field of research in non-speech audio processing. Most of current research in ESC focuses on designing deep models with special architectures tailored for specific audio datasets, which usually cannot exploit the intrinsic patterns in the data. However recent studies have surprisingly shown that transfer learning from models trained on ImageNet is a very effective technique in ESC. Herein, we propose SoundCLR, a supervised contrastive learning method for effective environment sound classification with state-of-the-art performance, which…
We investigate an method for quantifying city characteristics based on impressions of a sound environment. The quantification of the city characteristics will be beneficial to government policy planning, tourism projects, etc. In this study, we try to predict two soundscape impressions, meaning pleasantness and eventfulness, using sound data collected by the cloud-sensing method. The collected sounds comprise meta information of recording location using Global Positioning System. Furthermore, the soundscape impressions and sound-source features are separately assigned to the cloud-sensing soun…
Nanyang Technological University（南洋理工大学）；University of Surrey（萨里大学）
Continuously learning new classes without catastrophic forgetting is a challenging problem for on-device environmental sound classification given the restrictions on computation resources (e.g., model size, running memory). To address this issue, we propose a simple and efficient continual learning method. Our method selects the historical data for the training by measuring the per-sample classification uncertainty. Specifically, we measure the uncertainty by observing how the classification probability of data fluctuates against the parallel perturbations added to the classifier embedding. In…
Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties. The ConditionaL Neural Network (CLNN) is designed to consider the relational properties across frames in a temporal signal, and its extension the Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within the network, which enforces the network to learn in frequency bands rather than bins. Additionally, it automates the exploration of different feature combinations analogous to handcrafting the optimum combination o…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical $K$-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform (DWT). The DWT spectrograms are then augmented by a novel architecture for cycle-consistent generative adversarial network. This high-level augmentation bootstraps generated spectrograms in both intra and inter class manners by translating structural features from sample to sampl…
Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism is assigning contribution weights on different parts of features, namely channels, spectral or spatial contents, and temporal frames. In this paper, we propose an effective convolutional neural network structure with a multi-channel temporal attention (MCTA) block, which applies a temporal attention mechanism within each channel of the embedded features to extract channel-wise relevant temporal information. This mul…
Environmental Sound Deepfake Detection Challenge: An Overview
1 School of Electrical Engineering, KAIST, Daejeon, Republic of Korea 2 University of Melbourne, Australia, 3 Fortemedia Singapore, Singapore, 4 Xi’an University of Posts \& Telecommunications, Xi’an, China, 5 Xi'an Lianfeng Acoustic Technologies Co., Ltd., China
University of São Paulo（圣保罗大学）；University College Cork（科克大学）
This technical report details changes applied to a noise filter to facilitate its application and improve its results. The filter is applied to denoise natural sounds recorded in the wild and to generate an acoustic index used in soundscape analysis.
University of Cambridge（剑桥大学）；Jomo Kenyatta University of Agriculture and Technology（乔莫·肯雅塔农业技术大学）
Machine hearing of the environmental sound is one of the important issues in the audio recognition domain. It gives the machine the ability to discriminate between the different input sounds that guides its decision making. In this work we exploit the self-supervised contrastive technique and a shallow 1D CNN to extract the distinctive audio features (audio representations) without using any explicit annotations.We generate representations of a given audio using both its raw audio waveform and spectrogram and evaluate if the proposed learner is agnostic to the type of audio input. We further u…
The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network's links using a binary mask. Additionally, the masking automates the exploration of different feature combinations concurrently analogous to handcrafting the optimum combination of features for a recognition task. We have evaluated the MCLNN performance using the Urbansound8k dataset of environm…
ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
1 School of Electrical Engineering, KAIST, Daejeon, Republic of Korea 2 University of Melbourne, Australia 3 Fortemedia Singapore, Singapore 4 Xi'an University of Posts \& Telecommunications, Xi'an, China
The First Environmental Sound Deepfake Detection Challenge: Benchmarking Robustness, Evaluation, and Insights
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea（韩国成均馆大学电气工程学院）；University of Melbourne, Australia（墨尔本大学）；Fortemedia Singapore, Singapore（新加坡Fortemedia公司）；Xi’an University of Posts & Telecommunications, Xi’an, China（西安邮电大学）；Xi'an Lianfeng Acoustic Technologies Co., Ltd., China（西安联丰声学技术有限公司）
With the growth of the Internet of Things and the rise of Big Data, data processing and machine learning applications are being moved to cheap and low size, weight, and power (SWaP) devices at the edge, often in the form of mobile phones, embedded systems, or microcontrollers. The field of Cyber-Physical Measurements and Signature Intelligence (MASINT) makes use of these devices to analyze and exploit data in ways not otherwise possible, which results in increased data quality, increased security, and decreased bandwidth. However, methods to train and deploy models at the edge are limited, and…
Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Mcgill University（麦吉尔大学）
Environmental sound classification (ESC) is a challenging problem due to the unstructured spatial-temporal relations that exist in the sound signals. Recently, many studies have focused on abstracting features from convolutional neural networks while the learning of semantically relevant frames of sound signals has been overlooked. To this end, we present an end-to-end framework, namely feature pyramid attention network (FPAM), focusing on abstracting the semantically relevant features for ESC. We first extract the feature maps of the preprocessed spectrogram of the sound waveform by a backbon…
After its sweeping success in vision and language tasks, pure attention-based neural architectures (e.g. DeiT) are emerging to the top of audio tagging (AT) leaderboards, which seemingly obsoletes traditional convolutional neural networks (CNNs), feed-forward networks or recurrent networks. However, taking a closer look, there is great variability in published research, for instance, performances of models initialized with pretrained weights differ drastically from without pretraining, training time for a model varies from hours to weeks, and often, essences are hidden in seemingly trivial det…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal. Several convolutional layers are used to capture the signal's fine time structure and learn diverse filters that are relevant to the classification task. The proposed approach can deal with audio signals of any length as it splits the signal into overlapped frames using a sliding window. Different architectures considering several input sizes are evaluated, including the initialization of the first co…
Environmental Sound Deepfake Detection Using Deep-Learning Framework
Center for Digital Safety & Security, Austrian Institute of Technology (AIT)（数字安全与安全中心，奥地利技术研究所）；FPT University（FPT大学）；Ton Duc Thang University（-ton Duc Thang大学）；HCM University of Technology（胡志明理工大学）
