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Interspeech Conference

作者： Xia, Wei Koishida, Kazuhito Univ Texas Dallas Ctr Robust Speech Syst Dallas TX 75080 USA Microsoft Corp One Microsoft Way Redmond WA 98052 USA

In this study, we introduce a convolutional time-frequency-channel "Squeeze and Excitation" (tfc-SE) module to explicitly model inter-dependencies between the time-frequency domain and multiple channels. The tfc-SE module consists of two parts: tf-SE block and c-SE block which are designed to provide attention on time-frequency and channel domain, respectively, for adaptively recalibrating the input feature map. The proposed tfc-SE module, together with a popular Convolutional Recurrent Neural Network (CRNN) model, are evaluated on a multi-channel sound event detection task with overlapping audio sources: the training and test data are synthesized TUT sound events 2018 datasets, recorded with microphone arrays. We show that the tfc-SE module can be incorporated into the CRNN model at a small additional computational cost and bring significant improvements on sound event detection accuracy. We also perform detailed ablation studies by analyzing various factors that may influence the performance of the SE blocks. We show that with the best tfc-SE block, error rate (ER) decreases from 0.2538 to 0.2026, relative 20.17% reduction of ER, and 5.72% improvement of F1 score. The results indicate that the learned acoustic embeddings with the tfc-SE module efficiently strengthen time-frequency and channel-wise feature representations to improve the discriminative performance.

关键词： sound event detection squeece and excitation attention multichannel audio convolutional recurrent neural network

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sound event detection and Localization with Distance Estimation 32

Sound Event Detection and Localization with Distance Estimat...

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32nd European Signal Processing Conference (EUSIPCO)

作者： Krause, Daniel Aleksander Politis, Archontis Mesaros, Annamaria Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland

ISBN: (纸本)9789464593617;9798331519773

sound event detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA). While this task has numerous applications and has been extensively researched in recent years, it fails to provide full information about the sound source position. In this paper, we overcome this problem by extending the task to sound event detection, Localization with Distance Estimation (3D SELD). We study two ways of integrating distance estimation within the SELD core - a multi-task approach, in which the problem is tackled by a separate model output, and a single-task approach obtained by extending the multi-ACCDOA method to include distance information. We investigate both methods for the Ambisonic and binaural versions of STARSS23: Sony-TAU Realistic Spatial soundscapes 2023. Moreover, our study involves experiments on the loss function related to the distance estimation part. Our results show that it is possible to perform 3D SELD without any degradation of performance in sound event detection and DOA estimation.

关键词： sound event detection sound source localization sound distance estimation Ambisonics binaural recordings

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Joining sound event detection and Localization Through Spatial Segregation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 487-502页

作者： Trowitzsch, Ivo Schymura, Christopher Kolossa, Dorothea Obermayer, Klaus Tech Univ Berlin Dept Elect Engn & Comp Sci Neural Informat Proc Grp D-10623 Berlin Germany Ruhr Univ Bochum Dept Elect Engn & Informat Technol Cognit Signal Proc Grp D-44801 Bochum Germany

Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming coherent auditory objects and achieving a comprehensive spatial scene understanding suggests pursuing a joint solution of the two problems. This article presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system. Both tasks are joined through the use of spatial stream segregation which produces probabilistic time-frequency masks for individual sources attributable to separate locations, enabling segregated sound event detection operating on these streams. We use simulations of a comprehensive suite of test scenes with multiple co-occurring sound sources, and propose performance measures for systematic investigation of the impact of scene complexity on this segregated detection of sound types. Analyzing the effect of spatial scene arrangement, we show how a robot could facilitate high performance through optimal head rotation. Furthermore, we investigate the performance of segregated detection given possible localization error as well as error in the estimation of number of active sources. Our analysis demonstrates that the proposed approach is an effective method to obtain joint sound event location and type information under a wide range of conditions.

关键词： Feature extraction event detection Training Speech processing Time-frequency analysis Ear Computational modeling Acoustic scene analysis auditory modeling multi-conditional training robustness sound event detection sound source localization spatial stream segregation

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Adaptive Pooling Operators for Weakly Labeled sound event detection

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第11期26卷 2180-2193页

作者： McFee, Brian Salamon, Justin Bello, Juan Pablo NYU Mus & Audio Res Lab 550 1St Ave New York NY 10003 USA NYU Ctr Data Sci 550 1St Ave New York NY 10003 USA NYU Ctr Urban Sci & Progress Brooklyn NY 11201 USA

sound event detection (SED) methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. However, strong annotations of this type are both labor- and cost-intensive for human annotators to produce, which limits the practical scalability of SED methods. In this paper, we treat SED as a multiple instance learning (MIL) problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality. The models, however, must still produce temporally dynamic predictions, which must be aggregated (pooled) when comparing against static labels during training. To facilitate this aggregation, we develop a family of adaptive pooling operators-referred to as autopool-which smoothly interpolate between common pooling operators, such as min-, max-, or average-pooling, and automatically adapt to the characteristics of the sound sources in question. We evaluate the proposed pooling operators on three datasets, and demonstrate that in each case, the proposed methods outperform nonadaptive pooling operators for static prediction, and nearly match the performance of models trained with strong, dynamic annotations. The proposed method is evaluated in conjunction with convolutional neural networks, but can be readily applied to any differentiable model for time-series label prediction. While this paper focuses on SED applications, the proposed methods are general, and could be applied widely to MIL problems in any domain.

关键词： sound event detection machine learning multiple instance learning deep learning

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Multi-Branch Convolutional Macaron net for sound event detection

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 2972-2985页

作者： Chan, Teck Kai Chin, Cheng Siong Newcastle Univ Int Singapore Fac Sci Agr & Engn Singapore 599493 Singapore

sound event detection remains a challenging task due to the lack of strongly labeled data. While the use of weakly labeled and unlabeled data can alleviate this issue, most state-of-the-art (SOTA) utilized the Mean-Teacher approach, which requires training two identical models in a semi-supervised manner. Such methodology can have two critical limitations. Firstly, it can be computationally expensive if a very deep model is designed. Secondly, a model designed might only be optimal for either audio tagging or frame-level prediction but not both. Thus, using the Mean-Teacher approach may only allow a model to perform at its maximum potential for one of the tasks. However, the aforementioned issues can be circumvented by designing two different models where the less complex model provides the frame-level prediction while the more complex model provides the audio tags. To increase the accuracy of the models, we propose the use of Squeeze and Excite, meta-ACON, an improved Transformer encoding layer, a triple instance-level pooling approach (i.e., multi-branch pooling), and an improved cyclic learning scheme. Based on such a framework, the best system can achieve an event-based F1-score of 48.5%. By ensembling the top 5 models, the event-based F1-score increases to 50.4%. The proposed framework can achieve a minimum margin of over 12% against the baseline system while being competitive with the other SOTA.

关键词： Computational modeling Hidden Markov models Data models Predictive models Transformers Training Task analysis Cyclic learning macaron net multi-branch pooling sound event detection transformer

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Multi-Scale and Single-Scale Fully Convolutional Networks for sound event detection

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NEUROCOMPUTING 2021年 421卷 51-65页

作者： Wang, Yingbin Zhao, Guanghui Xiong, Kai Shi, Guangming Zhang, Yumeng Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China

Among various sound event detection (SED) systems, Recurrent Neural Networks (RNN), such as long short-term memory unit and gated recurrent unit, is used to capture temporal dependencies, but it is confined in its length of temporal dependencies, resulting in a failure to model sound events with long duration. What's more, RNN is incapable to process datasets in parallel, leading to low efficiency and low industrial value. Given these shortcomings, we propose to use dilated convolution (and causal dilated convolution) to capture temporal dependencies, as its great ability to ensure high time resolution and obtain longer temporal dependencies under the filter size and the network depth unchanged. In addition, dilated convolution can be parallelized, so it has higher efficiency and industrial value. Based on this, we propose Single-Scale Fully Convolutional Networks (SS-FCN) composed of convolutional neural networks and dilated convolutional networks, with the former to provide frequency invariance and the later to capture temporal dependencies. With the help of dilated convolution to control the length of temporal dependencies, we observe SS-FCN modeling a single length of temporal dependencies achieves superior detection performance for finite kinds of events. For better performance, we propose Multi-Scale Fully Convolutional Networks (MS-FCN), in which the feature fusion module is introduced to capture long short-term dependencies by fusing features with different length of temporal dependencies. The proposed methods achieve competitive performance on three main datasets with higher efficiency. The results show that SED systems based on Fully Convolutional Networks have further research value and potential. (c) 2020 Elsevier B.V. All rights reserved.

关键词： sound event detection Temporal dependencies Dilated convolution Single-Scale Fully Convolutional Networks Multi-Scale Fully Convolutional Networks

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A safety-oriented framework for sound event detection in driving scenarios

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APPLIED ACOUSTICS 2024年 215卷

作者： Castorena, Carlos Cobos, Maximo Lopez-Ballester, Jesus Ferri, Francesc J. Univ Valencia Dept Informat Burjassot 46100 Spain

The safety of drivers has become increasingly important in today's rapidly evolving transportation landscape, especially with the rise of autonomous and smart vehicles. This paper proposes a safety-oriented framework for sound event detection in smart vehicles using deep learning models. The goal of this framework is to increase driver awareness, prevent accidents, and provide acoustic forensic analysis. To achieve this, a meaningful taxonomy of event classes in a driving scenario is introduced, taking into account the event classes that are known to be related to major driving distractors. Based on this taxonomy, a dataset has been created to train and evaluate a fully-convolutional sound event detection model that was inspired by the well-known YOLO vision model. Experimental results demonstrated that the proposed model offers competitive results, outperforming a state-of-the-art baseline using recurrent connections. This comprehensive framework for sound event detection in smart vehicles aligns with the recommended directions for future mobility scenarios and has the potential to significantly improve the safety and performance of smart vehicles.

关键词： sound event detection Deep learning Driver safety Smart vehicles sound event taxonomy

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A Multi-Resolution CRNN-Based Approach for Semi-Supervised sound event detection in DCASE 2020 Challenge

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IEEE ACCESS 2021年 9卷 89029-89042页

作者： de Benito-Gorron, Diego Ramos, Daniel Toledano, Doroteo T. Univ Autonoma Madrid Escuela Politecn Super AUDIAS Res Grp Madrid 28049 Spain

sound event detection is a task with a rising relevance over the recent years in the field of audio signal processing, due to the creation of specific datasets such as Google AudioSet or DESED (Domestic Environment sound event detection) and the introduction of competitive evaluations like the DCASE Challenge (detection and Classification of Acoustic Scenes and events). The different categories of acoustic events can present diverse temporal and spectral characteristics. However, most approaches use a fixed time-frequency resolution to represent the audio segments. This work proposes a multi-resolution analysis for feature extraction in sound event detection, hypothesizing that different resolutions can be more adequate for the detection of different sound event categories, and that combining the information provided by multiple resolutions could improve the performance of sound event detection systems. Experiments are carried out over the DESED dataset in the context of the DCASE 2020 Challenge, concluding that the combination of up to 5 resolutions allows a neural network-based system to obtain better results than single-resolution models in terms of event-based F1-score in every event category and in terms of PSDS (Polyphonic sound detection Score). Furthermore, we analyze the impact of score thresholding in the computation of F1-score results, finding that the standard value of 0.5 is suboptimal and proposing an alternative strategy based in the use of a specific threshold for each event category, which obtains further improvements in performance.

关键词： Task analysis event detection Acoustics Measurement Feature extraction Time-frequency analysis Annotations sound event detection multi-resolution DCASE 2020 Task 4

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A Human-in-the-Loop System for sound event detection and Annotation

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2018年第2期8卷 1–23页

作者： Kim, Bongjun Pardo, Bryan Northwestern Univ 2133 Sheridan Rd Evanston IL 60208 USA

Labeling of audio events is essential for many tasks. However, finding sound events and labeling them within a long audio file is tedious and time-consuming. In cases where there is very little labeled data (e.g., a single labeled example), it is often not feasible to train an automatic labeler because many techniques (e.g., deep learning) require a large number of human-labeled training examples. Also, fully automated labeling may not show sufficient agreement with human labeling for many uses. To solve this issue, we present a humanin-the-loop sound labeling system that helps a user quickly label target sound events in a long audio. It lets a user reduce the time required to label a long audio file (e.g., 20 hours) containing target sounds that are sparsely distributed throughout the recording (10% or less of the audio contains the target) when there are too few labeled examples (e.g., one) to train a state-of-the-art machine audio labeling system. To evaluate the effectiveness of our tool, we performed a human-subject study. The results show that it helped participants label target sound events twice as fast as labeling them manually. In addition to measuring the overall performance of the proposed system, we also measure interaction overhead and machine accuracy, which are two key factors that determine the overall performance. The analysis shows that an ideal interface that does not have interaction overhead at all could speed labeling by as much as a factor of four.

关键词： Interactive machine learning sound event detection human-in-the-loop system

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Weak Supervised sound event detection Based on Puzzle CAM

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IEEE ACCESS 2023年 11卷 89290-89297页

作者： Cai, Xichang Gan, Yanggang Wu, Menglong Wu, Juan North China Univ Technol Sch Informat Beijing 100144 Peoples R China

The sound event detection method based on deep learning has achieved excellent performance. However, the training of high-performance sound event detection models depends on high-quality strong-label datasets, which brings great pressure and cost to the labeling work of datasets. In this paper, we propose a weakly supervised sound event detection method based on Puzzle Class Activation Map(CAM), which aims to obtain the timestamp information of sound events from a sound event classification network trained by weak labels. Specifically, the method discovers more complete regions of sound events by minimizing the difference between the CAM of the original features and the merged CAM of the separated feature blocks. CAM highlights the feature capture in the time dimension during the model decision-making process. We determine the occurrence time of the sound event based on the position of the frame where the feature is located, and use the model classification prediction to guide the CAM output to achieve weakly supervised sound event detection. Experiments on the Domestic Environment sound event detection Dataset demonstrate that our proposed method exhibits superior performance compared to the baseline system in the task of sound event detection. Specifically, The CAM of a single system achieves the best PSDS2 value of 0.732. Furthermore, when utilizing the CAM ensemble of multiple systems, the PSDS2 value improves to 0.751. Both of these values are higher than those achieved by the baseline system.

关键词： event detection Task analysis Convolutional neural networks Feature extraction Spectrogram Recurrent neural networks Class activation map object localization sound event detection weak supervision

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