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arXiv 2023年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The International Audio Laboratories Erlangen Germany The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra University of Coimbra Portugal The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called "predominant local pulses" (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2023, CC BY.

关键词： Dynamic programming

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An Analysis Method for Metric-Level Switching in Beat Tracking

arXiv

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arXiv 2022年

作者： Chiu, Ching-Yu Müller, Meinard Davies, Matthew E.P. Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan International Audio Laboratories Erlangen Germany Department of Informatics Engineering Centre for Informatics and Systems The University of Coimbra University of Coimbra Portugal Department of CSIE National Cheng Kung University Taiwan Taiwan AI Labs Taiwan

For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switch between several incorrect but perceptually plausible ones (e.g., half- or double-tempo). Existing evaluation metrics for beat tracking do not reflect such behaviors, as they typically assume a fixed relationship between the reference beats and estimated beats. In this paper, we propose a new performance analysis method, called annotation coverage ratio (ACR), that accounts for a variety of possible metric-level switching behaviors of beat trackers. The idea is to derive sequences of modified reference beats of all metrical levels for every two consecutive reference beats, and compare every sequence of modified reference beats to the subsequences of estimated beats. We show via experiments on three datasets of different genres the usefulness of ACR when being utilized alongside existing metrics, and discuss the new insights that can be gained. © 2022, CC BY.

关键词： Taps

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Acoustic and Textual Data Augmentation for Code-Switching Speech Recognition in Under-Resourced Language

Acoustic and Textual Data Augmentation for Code-Switching Sp...

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

作者： I-Ting Hsieh Chung-Hsien Wu Chun-Huang Wang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Tainan Taiwan

ISBN: (数字)9789881476883

ISBN: (纸本)9781728181301

Under-resourced and code-switching speech recognition have recently received research interest, resulting in several robust acoustic and language modeling approaches. As Taiwanese and Mandarin have been popularly and widely used in Taiwan, this paper aims to address the under-resourced and codeswitching issues. First, phone sharing between Taiwanese and Mandarin is employed for acoustic data augmentation to construct the acoustic models of Taiwanese speech recognizer. Regarding the lack of Taiwanese text corpus, this paper translates Mandarin corpus into Taiwanese corpus based on word-to-word translation. Moreover, additional translation rules for codeswitching text are manually designed. The augmented text corpus is then used for training the code-switching language models. In the experimental results, the word error rate for code-switching speech recognition was 26.02%, which was better than that trained by the pure Taiwanese corpus.

关键词： Speech recognition Training Acoustics Switches Mathematical model Speech coding Hidden Markov models

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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

arXiv

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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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Recent advances in wearable electrochemical biosensors towards technological and material aspects

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Biosensors and Bioelectronics: X 2024年 19卷

作者： Hosseinzadeh Fakhr, Mahan Lopez Carrasco, Ivan Belyaev, Dmitry Kang, Jihun Shin, YeHyeon Yeo, Jong-Souk Koh, Won-Gun Ham, Jeongwoo Michaelis, Alexander Opitz, Joerg Beshchasna, Natalia Fraunhofer Institute for Ceramic Technologies and Systems IKTS Maria-Reiche-Straße 2 Dresden01109 Germany Faculty of Mechanical Science and Engineering Dresden University of Technology Dresden Germany School of Integrated Technology College of Computing Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of Department of Chemical and Biomolecular Engineering Yonsei University Seoul Korea Republic of

The next generation of wearable biosensors comes with the latest advancements in biosensor technology. Soft and stretchable electrode materials like hydrogels with the similar functionalities of human tissue including stretchability, self-healability, and responsiveness to different stimuli have emerged as the most versatile materials in wearable electronics. The incorporation of conductive nanofillers is found to enhance the sensitivity of the electrochemical biosensors significantly. Microfluidic technology has reduced the volume of samples and reagents required for the analysis, allowing continuous biomedical monitoring from a drop of biofluid. In this paper, the most advanced progress in electrochemical wearable platforms that can noninvasively and continuously monitor the biochemical markers in body fluids for the diagnosis and health management is reviewed. Innovation in microelectronics, modification, fabrication technologies, and detection methods are the main focus of the discussion. In particular, hydrogel-based sensors and microfluidic systems as the latest technology trends in wearable detection are discussed in detail. Integration of miniaturized electrochemical wearable biosensors with wireless technology as a great promise for real-time healthcare monitoring and point-of-care (POC) diagnostics is also summarized. Finally, we outline the most advanced wearable biosensors with optimized material and design as well as key challenges that need to be addressed to improve sensing performance (accuracy, sensitivity, selectivity, stability), portability (miniaturized size and light weight), and flexibility of the wearable biosensors. © 2024 The Authors

关键词： Body fluids

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis

Learning Adaptation and Generalization from Human-Inspired M...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Yi-Hsin You Chi-Wei Feng Yen-Min Chen Bo-Yi Kuo Academia Sinica Institute of Information Science Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Taiwan University Taipei Taiwan

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a human-inspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.

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Source separation-based data augmentation for improved joint beat and downbeat tracking

arXiv

引用

arXiv 2021年

作者： Chiu, Ching-Yu Ching, Joann Hsiao, Wen-Yi Chen, Yu-Hua Su, Alvin Wen-Yu Yang, Yi-Hsuan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan Research Center for IT Innovation Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Dept. CSIE National Cheng Kung University

Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years. In training such deep learning based models, data augmentation has been found an important technique. However, existing data augmentation methods for this task mainly target at balancing the distribution of the training data with respect to their tempo. In this paper, we investigate another approach for data augmentation, to account for the composition of the training data in terms of the percussive and non-percussive sound sources. Specifically, we propose to employ a blind drum separation model to segregate the drum and non-drum sounds from each training audio signal, filtering out training signals that are drumless, and then use the obtained drum and non-drum stems to augment the training data. We report experiments on four completely unseen test sets, validating the effectiveness of the proposed method, and accordingly the importance of drum sound composition in the training data for beat and downbeat tracking. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

引用

arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

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Combining Deep Embeddings of Acoustic and Articulatory Features for Speaker Identification

Combining Deep Embeddings of Acoustic and Articulatory Featu...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Qian-Bei Hong Chung-Hsien Wu Hsin-Min Wang Chien-Lin Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan PingAn AI Lab Palo Alto CA USA

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

In this study, deep embedding of acoustic and articulatory features are combined for speaker identification. First, a convolutional neural network (CNN)-based universal background model (UBM) is constructed to generate acoustic feature (AC) embedding. In addition, as the articulatory features (AFs) represent some important phonological properties during speech production, a multilayer perceptron (MLP)-based AF embedding extraction model is also constructed for AF embedding extraction. The extracted AC and AF embeddings are concatenated as a combined feature vector for speaker identification using a fully-connected neural network. This proposed system was evaluated by three corpora consisting of King-ASR, LibriSpeech and SITW, and the experiments were conducted according to the properties of the datasets. We adopted all three corpora to evaluate the effect of AF embedding, and the results showed that combining AF embedding into the input feature vector improved the performance of speaker identification. The LibriSpeech corpus was used to evaluate the effect of the number of enrolled speakers. The proposed system achieved an EER of 7.80% outperforming the method based on x-vector with PLDA (8.25%). And we further evaluated the effect of signal mismatch using the SITW corpus. The proposed system achieved an EER of 25.19%, which outperformed the other baseline methods.

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Statistics Pooling Time Delay Neural Network Based on X-Vector for Speaker Verification

Statistics Pooling Time Delay Neural Network Based on X-Vect...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066322

This paper aims to improve speaker embedding representation based on x-vector for extracting more detailed information for speaker verification. We propose a statistics pooling time delay neural network (TDNN), in which the TDNN structure integrates statistics pooling for each layer, to consider the variation of temporal context in frame-level transformation. The proposed feature vector, named as statsvector, are compared with the baseline x-vector features on the VoxCeleb dataset and the Speakers in the Wild (SITW) dataset for speaker verification. The experimental results showed that the proposed stats-vector with score fusion achieved the best performance on VoxCeleb1 dataset. Furthermore, considering the interference from other speakers in the recordings, we found that the proposed statsvector efficiently reduced the interference and improved the speaker verification performance on the SITW dataset.

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