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检索条件"主题词=Sound Event Detection"

共 295 条记录，以下是81-90 订阅

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GUIDED LEARNING FOR WEAKLY-LABELED SEMI-SUPERVISED sound event detection

GUIDED LEARNING FOR WEAKLY-LABELED SEMI-SUPERVISED SOUND EVE...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lin, Liwei Wang, Xiangdong Liu, Hong Qian, Yueliang Chinese Acad Sci Inst Comp Technol Bejing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781509066315

We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data. The guidance is guaranteed by the audio tagging performance gap of the two models. In the meantime, the student model liberated from the trade-off is able to provide more excellent boundary detection results. We propose a principle to design such two models based on the relation between the temporal compression scale and the two sub-targets. We also propose an end-to-end semi-supervised learning process for these two models to enable their abilities to rise alternately. Experiments on the DCASE2018 Task4 dataset show that our approach achieves competitive performance.

关键词： sound event detection weakly-labeled semi-supervised learning neural networks

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FINE-TUNE THE PRETRAINED ATST MODEL FOR sound event detection 49

FINE-TUNE THE PRETRAINED ATST MODEL FOR SOUND EVENT DETECTIO...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shao, Nian Li, Xian Li, Xiaofei Zhejiang Univ Hangzhou Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China Westlake Inst Adv Study Inst Adv Technol Hangzhou Peoples R China

ISBN: (纸本)9798350344868;9798350344851

sound event detection (SED) often suffers from the data deficiency problem. Recent SED systems leverage the large pretrained self-supervised learning (SelfSL) models to mitigate such restriction, where the pretrained models help to produce more discriminative features for SED. However, the pretrained models are regarded as a frozen feature extractor in most systems, and fine-tuning of the pretrained models has been rarely studied. In this work, we study the fine-tuning method of the pretrained models for SED. We introduce frame-level audio teacher-student transformer model (ATST-Frame), our newly proposed SelfSL model, to the SED system. ATST-Frame was especially designed for learning frame-level representations of audio signals and obtained state-of-the-art (SOTA) performances on a series of downstream tasks. We then propose a fine-tuning method for ATST-Frame using both (in-domain) unlabelled and labelled SED data. Our experiments show that, the proposed method overcomes the overfitting problem when fine-tuning the large pretrained network, and our SED system obtains new SOTA results of 0.587/0.812 PSDS1/PSDS2 on the DCASE challenge task 4 dataset.

关键词： sound event detection self-supervised learning ATST fine-tuning pretrained model

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Leveraging Audio-Tagging Assisted sound event detection using Weakified Strong Labels and Frequency Dynamic Convolutions 22

Leveraging Audio-Tagging Assisted Sound Event Detection usin...

引用

22nd IEEE Statistical Signal Processing Workshop (SSP)

作者： Khandelwal, Tanmay Das, Rohan Kumar Koh, Andrew Chng, Eng Siong Fortemedia Singapore Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665452458

Jointly learning from a small labeled set and a larger unlabeled set is an active research topic under semi-supervised learning (SSL). In this paper, we propose a novel SSL method based on a two-stage framework for leveraging a large unlabeled in-domain set. Stage-1 of our proposed framework focuses on audio-tagging (AT), which assists the sound event detection (SED) system in Stage-2. The AT system is trained utilizing a strongly labeled set converted into weak predictions referred to as weakified set, a weakly labeled set, and an unlabeled set. This AT system then infers on the unlabeled set to generate reliable pseudo-weak labels, which are used with the strongly and weakly labeled set to train a frequency dynamic convolutional recurrent neural network-based SED system at Stage-2 in a supervised manner. Our system outperforms the baseline by 45.5% in terms of polyphonic sound detection score on the DESED real validation set.

关键词： semi-supervised learning sound event detection two-stage setup pseudo-labels

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Adaptive Few-Shot Learning Algorithm for Rare sound event detection

Adaptive Few-Shot Learning Algorithm for Rare Sound Event De...

引用

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Zhao, Chendong Wang, Jianzong Li, Leilai Qu, Xiaoyang Xiao, Jing Ping An Technol Shenzhen Co Ltd Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

sound event detection is to infer the event by understanding the surrounding environmental sounds. Due to the scarcity of rare sound events, it becomes challenging for the well-trained detectors which have learned too much prior knowledge. Meanwhile, few-shot learning methods promise a good generalization abaility when facing a new limited-data task. Recent approaches have achieved promising results in this field. However, these approaches treat each support example independently, ignoring the information of other examples from the whole task. Because of this, most of previous methods are constrained to generate a same feature embedding for all test-time tasks, which is not adaptive to each inputted data. In this work, we propose a novel task-adaptive module which is easy to plant into any metric-based few-shot learning frameworks. The module could identify the task-relevant feature dimension. Incorporating our module improves the performance considerably on two datasets over baseline methods, especially for the transductive propagation network. Such as +6.8% for 5-way 1-shot accuracy on ESC-50, and +5.9% on noiseESC-50. We investigate our approach in the domain-mismatch setting and also achieve better results than previous methods.

关键词： sound event detection Few-shot learning Data augmentation Deep learning

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A Task-Specific Meta-Learning Framework for Few-Shot sound event detection 24

A Task-Specific Meta-Learning Framework for Few-Shot Sound E...

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IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)

作者： Zhang, Tianyang Yang, Liping Gu, Xiaohua Wang, Yuyang Chongqing Univ Key Lab Optoelect Technol & Syst MOE Chongqing Peoples R China Chongqing Univ Sci & Technol Sch Elect Engn Chongqing Peoples R China

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

Meta-learning is extensively used for few-shot learning. Prototypical Network (ProtoNet) has been proved to perform well for few-shot sound event detection. ProtoNet as a metalearning method consists of two stages: meta-train and metatest. During meta-train stage, an embedded network is trained on base-classes using episode training strategy. During metatest stage, the embedded network is transferred directly to unseen classes during meta-train. This style of transference is task-agnostic: the embedded network may not learn the optimal discrimination embedding features for specific tasks including unseen classes. In this paper, we propose a task-specific meta-learning framework (TSMLF) for few-shot sound event detection, which makes embedded network learn discrimination embedding features for specific tasks. TSMLF inherits the metatrain process of ProtoNet. During meta-test stage, the framework enables embedded network to learn discriminative embedding features by inter-class and intra-class differences. Concretely, we calculate the inter-class and intra-class distance that support set sound samples. Maximizing inter-class distance and minimizing intra-class distance (MIMI) are used as a criteria to fine-tune embedded network for specific tasks. In addition, due to the small-scaled support set of meta-test, similar sound samples are easily excessively clustered during fine-tuning. We set a distance constraint on intra-class distance to avoid overfitting of embedded network. The proposed framework is evaluated using few-shot dataset of detection and Classification of Acoustic Scenes and events challenges 2022 (DCASE2022) task 5. Extensive ablation experimental results validate that all components of TSMLF can provide positive effects on few-shot sound event detection.

关键词： Prototypical network sound event detection Few-shot Task-specific Inter-class and intra-class

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A FRAMEWORK FOR THE ROBUST EVALUATION OF sound event detection

A FRAMEWORK FOR THE ROBUST EVALUATION OF SOUND EVENT DETECTI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bilen, Cagdas Ferroni, Giacomo Tuveri, Francesco Azcarreta, Juan Krstulovic, Sacha Audio Analyt AA Labs 2 Quayside Cambridge CB5 8AB England

ISBN: (纸本)9781509066315

This work defines a new framework for performance evaluation of polyphonic sound event detection (SED) systems, which overcomes the limitations of the conventional collar-based event decisions, event F-scores and event error rates. The proposed framework introduces a definition of event detection that is more robust against labelling subjectivity. It also resorts to polyphonic receiver operating characteristic (ROC) curves to deliver more global insight into system performance than F1-scores, and proposes a reduction of these curves into a single polyphonic sound detection score (PSDS), which allows system comparison independently from operating points (OPs). The presented method also delivers better insight into data biases and classification stability across sound classes. Furthermore, it can be tuned to varying applications in order to match a variety of user experience requirements. The benefits of the proposed approach are demonstrated by re-evaluating the baseline and two of the top-performing systems from DCASE 2019 Task 4.

关键词： sound event detection SED evaluation metrics sound recognition polyphonic sound detection score PSDS

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A JOINT SEPARATION-CLASSIFICATION MODEL FOR sound event detection OF WEAKLY LABELLED DATA

A JOINT SEPARATION-CLASSIFICATION MODEL FOR SOUND EVENT DETE...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kong, Qiuqiang Xu, Yong Wang, Wenwu Plumbley, Mark D. Univ Surrey Ctr Vis Speech & Signal Proc Surrey England

ISBN: (纸本)9781538646588

Source separation (SS) aims to separate individual sources from an audio recording. sound event detection (SED) aims to detect sound events from an audio recording. We propose a joint separation-classification (JSC) model trained only on weakly labelled audio data, that is, only the tags of an audio recording are known but the time of the events are unknown. First, we propose a separation mapping from the time-frequency (T-F) representation of an audio to the T-F segmentation masks of the audio events. Second, a classification mapping is built from each T-F segmentation mask to the presence probability of each audio event. In the source separation stage, sources of audio events and time of sound events can be obtained from the T-F segmentation masks. The proposed method achieves an equal error rate (EER) of 0.14 in SED, outperforming deep neural network baseline of 0.29. Source separation SDR of 8.08 dB is obtained by using global weighted rank pooling (GWRP) as probability mapping, outperforming the global max pooling (GMP) based probability mapping giving SDR at 0.03 dB. Source code of our work is published.

关键词： sound event detection source separation weakly labelled data

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Augmented Strategy For Polyphonic sound event detection

Augmented Strategy For Polyphonic Sound Event Detection

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Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Wang, Bolun Fu, Zhong-Hua Wu, Hao Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Xian IFLYTEK Hyper Brain Informat Technol Co Ltd Xian Peoples R China

ISBN: (纸本)9781728132488

sound event detection is an important issue for many applications like audio content retrieval, intelligent monitoring, and scene-based interaction. The traditional studies on this topic are mainly focusing on identification of single sound event class. However, in real applications, several sound events usually happen concurrently and with different durations. That leads to a new detection task on polyphonic sound event classification along with event time boundaries. In this paper, we propose an augmented strategy for this task, which faces challenges of a large amount of unbalanced and weakly labelled training data. Specifically, the strategy includes data augmentation to enrich training set to eliminate data unbalance, a new loss function that combines cross entropy and F-score, and model fusion to integrate the powers of different classifiers. The performance of the strategy is validated on DCASE2019 dataset, and both the event and segment detections are significantly improved over the baseline system.

关键词： sound event detection Data augmentation Model fusion

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Learning How to Listen: A Temporal-Frequential Attention Model for sound event detection 20

Learning How to Listen: A Temporal-Frequential Attention Mod...

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Interspeech Conference

作者： Shen, Yu-Han He, Ke-Xin Zhang, Wei-Qiang Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

In this paper, we propose a temporal-frequential attention model for sound event detection (SED). Our network learns how to listen with two attention models: a temporal attention model and a frequential attention model. Proposed system learns when to listen using the temporal attention model while it learns where to listen on the frequency axis using the frequential attention model. With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. Our proposed method is demonstrated on the task 2 of detection and Classification of Acoustic Scenes and events (DCASE) 2017 Challenge and outperforms state-of-the-art methods.

关键词： sound event detection convolutional neural network recurrent neural network attention model temporal-frequential attention

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JOINT ACOUSTIC AND CLASS INFERENCE FOR WEAKLY SUPERVISED sound event detection 44

JOINT ACOUSTIC AND CLASS INFERENCE FOR WEAKLY SUPERVISED SOU...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kothinti, Sandeep Imoto, Keisuke Chakrabarty, Debmalya Sell, Gregory Watanabe, Shinji Elhilali, Mounya Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Ritsumeikan Univ Coll Informat Sci & Engn Shiga Japan Johns Hopkins Univ Human Language Technol Ctr Excellence Baltimore MD USA

ISBN: (纸本)9781479981311

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 DCASE challenge presents an event detection task that requires accuracy in both segmentation and recognition of events while providing only weakly labeled training data. Supervised methods can produce accurate event labels but are limited in event segmentation when training data lacks event timestamps. On the other hand, unsupervised methods that model the acoustic properties of the audio can produce accurate event boundaries but are not guided by the characteristics of event classes and sound categories. We present a hybrid approach that combines an acoustic-driven event boundary detection and a supervised label inference using a deep neural network. This framework leverages benefits of both unsupervised and supervised methodologies and takes advantage of large amounts of unlabeled data, making it ideal for large-scale weakly labeled event detection. Compared to a baseline system, the proposed approach delivers a 15% absolute improvement in F-score, demonstrating the benefits of the hybrid bottom-up, top-down approach.

关键词： sound event detection unsupervised learning weakly labeled data restricted Boltzmann machine conditional restricted Boltzmann machine

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