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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets"

共 9200 条记录，以下是161-170 订阅

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Guitar-TECHS: An Electric Guitar dataset Covering Techniques, Musical Excerpts, Chords and Scales Using a Diverse Array of Hardware

Guitar-TECHS: An Electric Guitar Dataset Covering Techniques...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hegel Pedroza Wallace Abreu Ryan M. Corey Iran R. Roman Dept. of Art & Humanities UAM Lerma Estado de México México Signals Media & Telecom. Lab UFRJ Rio de Janeiro Brasil Discovery Partners Institute UIC Chicago United States School of EECS QMUL London United Kingdom

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Guitar-related machine listening research involves tasks like timbre transfer, performance generation, and automatic transcription. However, small datasets often limit model robustness due to insufficient acoustic diversity and musical content. To address these issues, we introduce Guitar-TECHS, a comprehensive dataset featuring a variety of guitar techniques, musical excerpts, chords, and scales. These elements are performed by diverse musicians across various recording settings. Guitar-TECHS incorporates recordings from two stereo microphones: an egocentric microphone positioned on the performer’s head and an exocentric microphone placed in front of the performer. It also includes direct input recordings and microphoned amplifier outputs, offering a wide spectrum of audio inputs and recording qualities. All signals and MIDI labels are properly synchronized. Its multi-perspective and multi-modal content makes Guitar-TECHS a valuable resource for advancing data-driven guitar research, and to develop robust guitar listening algorithms. We provide empirical data to demonstrate the dataset’s effectiveness in training robust models for Guitar Tablature Transcription.

关键词： Training signal processing algorithms Acoustics Robustness Magnetic heads Machine listening Timbre Synchronization Speech processing Microphones

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Text-Aware Adapter for Few-Shot Keyword Spotting

Text-Aware Adapter for Few-Shot Keyword Spotting

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Youngmoon Jung Jinyoung Lee Seungjin Lee Myunghun Jung Yong-Hyeok Lee Hoon-Young Cho AI Solution Team Samsung Research Seoul South Korea

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room for improvement in target keyword performance. In this work, we propose a novel few-shot transfer learning method, called text-aware adapter (TA-adapter), designed to enhance a pre-trained flexible KWS model for specific keywords with limited speech samples. To adapt the acoustic encoder, we leverage a jointly pre-trained text encoder to generate a text embedding that acts as a representative vector for the keyword. By fine-tuning only a small portion of the network while keeping the core components’ weights intact, the TA-adapter proves highly efficient for few-shot KWS, enabling a seamless return to the original pre-trained model. In our experiments, the TA-adapter demonstrated significant performance improvements across 35 distinct keywords from the Google Speech Commands V2 dataset, with only a 0.14% increase in the total number of parameters.

关键词： Adaptation models Transfer learning Speech enhancement signal processing Acoustics Vectors Text to speech Internet Synthetic data

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Meta-Learning for Finger Vein Recognition in Internet of Things Smart Home Security

Meta-Learning for Finger Vein Recognition in Internet of Thi...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hengyi Ren Lijuan Sun Jinting Ren Xing Li Ying Cao College of Information Science and Technology Nanjing Forestry University Nanjing China College of Computer Nanjing University of Posts and Telecommunications Nanjing China College of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, convolutional neural networks for finger vein recognition have gained attention, but their application in IoT smart home security is underexplored. Existing methods typically require networks to identify all categories in a dataset, leading to high parameter demands, which is inefficient given the small, dynamic user groups (3-5 users) in smart homes. To address this, we propose a finger vein recognition system based on meta-learning. Our approach frames recognition as a meta-learning task, introducing a dynamic, exponentially-weighted multistep loss optimization to enhance the model-agnostic meta-learning process. This allows quick adaptation to new tasks with minimal data. Additionally, we design an adaptive recognition scheme that updates network parameters without altering the structure for various users. Experiments on public datasets confirm the effectiveness of our system in IoT smart home security, achieving excellent recognition performance.

关键词： Metalearning Fingers Smart homes Speech recognition signal processing Security Internet of Things Convolutional neural networks Speech processing Optimization

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Climate Downscaling Using Neural Operator: Spatiotemporal Multimodal Fusion Operator with State-Query Coupled Kernel

Climate Downscaling Using Neural Operator: Spatiotemporal Mu...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haodi Zhang Yichi Wang Yifan Jian Jiahui Jiang Zhaohai Bai Lin Ma College of Computer Science and Software Engineering Shenzhen University Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Climate downscaling is crucial for detailed small- scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State- Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-of-the-art performance and provides strong support for climate downscaling and the planning of climate-related strategies.

关键词： Couplings signal processing Encoding Robustness Spatiotemporal phenomena Planning Kernel Speech processing Forecasting Meteorology

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Source-Aware Spatial Self-Supervision for Sound Event Localization and Detection

Source-Aware Spatial Self-Supervision for Sound Event Locali...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yuto Nozaki Yoshiaki Bando Masaki Onishi National Institute of Advanced Industrial Science and Technology Japan Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper presents a source-aware spatial self-supervised learning (SSL) method for sound event localization and detection (SELD) based on neural blind source separation (BSS). SELD involves estimating both the temporal activations of sound classes and their directions of arrival (DOAs) from multichannel audio signals. While deep neural networks have been successful in SELD, their reliance on spatial supervision, including class labels and DOAs, makes annotation challenging and time-intensive. To address this limitation, we propose self-supervised pre-training to separate and localize moving sound sources by maximizing a log-marginal posterior probability of a neural BSS model. This model, called neural full-rank spatial covariance analysis, requires only multichannel mixture signals for training. Our BSS-based framework explicitly trains the model to distinguish multiple sources in a mixture, enabling it to predict DOAs of multiple sound events. The proposed method thus enables us to train a SELD model with a small amount of annotation. The experiments with the STARSS23 dataset show that our method outperforms baseline SELD models trained with a limited amount of supervised data.

关键词： Location awareness Training Direction-of-arrival estimation Annotations Self-supervised learning signal processing Predictive models Blind source separation Spatial databases Speech processing

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Mask Augmentation For Tumor Classification In Medical Images

Mask Augmentation For Tumor Classification In Medical Images

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chun-Chieh Weng Huan-Yu Chen Jing-Tong Tzeng Ching-Heng Lin Po-Chih Kuo Chi-Chun Lee Department of Electrical Engineering National Tsing Hua University Taiwan College of Semiconductor Research National Tsing Hua University Taiwan Department of Medical Research Taichung Veteran General Hospital Taichung Taiwan Department of Computer Science National Tsing Hua University Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Tumor detection and classification in medical images are critical for guiding patient management and treatment decisions. However, accurate segmentation and classification of tumors remain challenging due to their small size relative to the overall image. Existing approaches often face difficulties with limited data and potential segmentation errors, resulting in suboptimal performance in real-world applications. To address these challenges, we propose a novel approach leveraging the Segmentation Mask Augmentation (SMA) framework. Our framework enhances the robustness of tumor classification models by generating diverse and imprecise segmentation masks during training, thereby simulating real-world scenarios. Experimental results across four distinct datasets demonstrate the effectiveness of our approach. Our framework presents a promising solution for robust tumor classification, with potential implications for improving clinical diagnosis and patient management.

关键词： Training Image segmentation Accuracy Decision making signal processing Robustness Speech processing Medical diagnostic imaging Faces Tumors

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Self-supervised Learning for Acoustic Few-Shot Classification

Self-supervised Learning for Acoustic Few-Shot Classificatio...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jingyong Liang Bernd Meyer Issac Ning Lee Thanh-Toan Do Information Technology Monash University Melbourne Australia Engineering Monash University Melbourne Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Labelled data are limited and self-supervised learning is one of the most important approaches for reducing labelling requirements. While it has been extensively explored in the image domain, it has so far not received the same amount of attention in the acoustic domain. Yet, reducing labelling is a key requirement for many acoustic applications. Specifically in bioacoustic, there are rarely sufficient labels for fully supervised learning available. This has led to the widespread use of acoustic recognisers that have been pre-trained on unrelated data for bioacoustic tasks. We posit that training on the actual task data and combining self-supervised pre-training with few-shot classification is a superior approach that has the ability to deliver high accuracy even when only a few labels are available. To this end, we introduce and evaluate a new architecture that combines CNN-based preprocessing with feature extraction based on state space models (SSMs). This combination is motivated by the fact that CNN-based networks alone struggle to capture temporal information effectively, which is crucial for classifying acoustic signals. SSMs, specifically S4 and Mamba, on the other hand, have been shown to have an excellent ability to capture long-range dependencies in sequence data. We pre-train this architecture using contrastive learning on the actual task data and subsequent fine-tuning with an extremely small amount of labelled data. We evaluate the performance of this proposed architecture for (n-shot, n-way) classification on standard benchmarks as well as real-world data. Our evaluation shows that it outperforms state-of-the-art architectures on the few-shot classification problem.

关键词： Training Accuracy Supervised learning LoRa Feature extraction Acoustics Labeling data mining Speech processing Standards

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Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding

Diversified Augmentation with Domain Adaptation for Debiased...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Junlong Ren Gangjian Zhang Haifeng Sun Hao Wang The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Temporal sentence grounding in videos (TSGV) faces challenges due to public TSGV datasets containing significant temporal biases, which are attributed to the uneven temporal distributions of target moments. Existing methods generate augmented videos, where target moments are forced to have varying temporal locations. However, since the video lengths of the given datasets have small variations, only changing the temporal locations results in poor generalization ability in videos with varying lengths. In this paper, we propose a novel training framework complemented by diversified data augmentation and a domain discriminator. The data augmentation generates videos with various lengths and target moment locations to diversify temporal distributions. However, augmented videos inevitably exhibit distinct feature distributions which may introduce noise. To address this, we design a domain adaptation auxiliary task to diminish feature discrepancies between original and augmented videos. We also encourage the model to produce distinct predictions for videos with the same text queries but different moment locations to promote debiased training. Experiments on Charades-CD and ActivityNet-CD datasets demonstrate the effectiveness and generalization abilities of our method in multiple grounding structures, achieving state-of-the-art results.

关键词： Training Adaptation models Grounding Noise Predictive models data augmentation Robustness Speech processing Faces Videos

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signal and data processing of small targets 1998

Signal and Data Processing of Small Targets 1998

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signal and data processing of small targets 1998

The proceedings containes 51 papers from the journal on signal and data processing of small targets 1998. The topics discussed include: radar sea reflection for low-e targets;small static and dynamic target detection in nontransparent medium and opaque water;common database for tracker compensation and theoretical design of trackers for tracking probability enhancement.

关键词： Radar target recognition

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signal and data processing of small targets 1996

Signal and Data Processing of Small Targets 1996

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signal and data processing of small targets 1996

The proceedings contains 54 papers. Following topics are discussed: signal processing;weak-target detection;signal and track processing;tracking - association and filtering;multiple-sensor, multiple-target tracking;da... 详细信息

ISBN: (纸本)0819421405

关键词： Tracking (position)

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