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MetaSTC: A Backbone Agnostic Spatio-Temporal Framework for Traffic Forecasting 24

MetaSTC: A Backbone Agnostic Spatio-Temporal Framework for T...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Xu, Kexin Yu, Zhemeng Gao, Yucen Zhang, Songjian Fang, Jun Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Didi Chuxing Technology Co. Beijing China

ISBN: (纸本)9798331506681

Traffic flow prediction is a critical issue in transportation engineering and presents distinct challenges when handling large-scale datasets in the real world. Existing complex spatio-temporal forecasting paradigms use the same parameters to fit traffic sequences with varying spatio-temporal features, and tend to train an average performance model over different time series. This approach greatly reduces their accuracy when applied to larger road networks. Moreover, the significant differences in traffic data distribution from one city to another can also pose great challenges. The same model may be excellent for one city and mediocre when applied to another. To this end, we propose a Meta Backbone Agnostic Spatio-Temporal Clustering Framework for Traffic Forecasting on Large-Scale Road Networks named MetaSTC. We tackle the disparities of spatio-temporal features of traffic flow through a spatio-temporal clustering-based strategy. We design meta-learner for large-scale road network that dynamically extracts the shared information across roads in the same sub-task. In this way, the model can represent task-specific details with a simpler model and make quick and accurate predictions. Our paradigm is backbone-agnostic and can be combined with different traffic prediction models, solving the problem caused by the difference in data distribution. Extensive experimental results conducted on real-world traffic dataset demonstrate the high accuracy and computational efficiency of our model over SOTA approaches. © 2024 IEEE.

关键词： Large datasets

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DBAugur: An Adversarial-based Trend Forecasting System for Diversified Workloads 39

DBAugur: An Adversarial-based Trend Forecasting System for D...

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Gao, Yuanning Huang, Xiuqi Zhou, Xuanhe Gao, Xiaofeng Li, Guoliang Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science TsingHua University Beijing China

ISBN: (纸本)9798350322279

Trend forecasting is vital to optimize the workload performance. It becomes even more urgent with an increasing number of applications and database configurations. However, DBAs mainly target at historical workloads and may give suboptimal configuration advice when the workload trends have changed. Although there are some studies on trend forecasting, they have several limitations. First, they mainly predict the changes of query numbers, which do not combine other critical factors (e.g., disk utilization) and cannot fully reflect the future workload trends. Besides, there are numerous queries in the workloads and exact clustering algorithms like K-means cannot effectively merge similar queries which contain noises like time shifts. Second, basic machine learning models like RNN may have relatively low prediction accuracy on complex workloads (e.g., no cycles but random bursts). Third, real-world workloads may have diverse patterns, while previous models cannot efficiently and reliably predict for all the different workload *** address these challenges, we propose a trend forecasting system (DBAugur) that utilizes adversarial neural networks to predict the trends of different workloads. First, DBAugur collects the important features (e.g., queries, resource metrics) to characterize workloads, and reduces the number of involved queries by separately merging similar queries based on the SQL semantics and trend patterns. Second, DBAugur utilizes Generative Adversarial Networks (GANs) to capture the latent patterns, correlations between different metrics, and occasional bursts within the complicated and time-varying workloads. Moreover, we further propose a time-sensitive ensemble algorithm that takes advantage of various machine learning models (e.g., generative models, convolutional models, feed-forward models) to accommodate the various workload patterns. The experimental results show that DBAugur outperformed state-of-the-art methods on various real-world wor

关键词： Forecasting

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Predictive Skim: Contrastive Predictive Coding for Low-Latency Online Speech Separation 48

Predictive Skim: Contrastive Predictive Coding for Low-Laten...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Chenda Wu, Yifei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering China

ISBN: (纸本)9781728163277

In online speech separation, there is a trade-off between inherent latency and speech separation performance. When processing the current input audio, looking ahead to more future context usually brings better speech separation performance but increases the algorithm latency, and vice versa. In the requirements of extremely low latency, the future context is expensive for the algorithm latency and may not be available. In this work, we apply the contrastive predictive coding (CPC) method to the previously proposed online Skipping Memory (SkiM) speech separation model, which is a low-latency model for online speech separation. During the training stage, the SkiM model is required to predict the future memory states given the history memory. By using CPC training, the predictive SkiM model shows stronger causal sequence modeling capacity in the online speech separation task. In addition, we explore a local context codec (LCC) method to reduce the computational cost, and we make qualitative analyses on it. Our best online predictive SkiM equipped with CPC and LCC gets 15.5 dB SI-SNR improvement on WSJ02-mix benchmark with 3-ms actual latency tested on a single-core CPU, which should be the state-of-the-art results among causal models. © 2023 IEEE.

关键词： Economic and social effects

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Fast-Hubert: an Efficient Training Framework for Self-Supervised Speech Representation Learning

Fast-Hubert: an Efficient Training Framework for Self-Superv...

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Yang, Guanrou Ma, Ziyang Zheng, Zhisheng Song, Yakun Niu, Zhikang Chen, Xie Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China

ISBN: (纸本)9798350306897

Recent years have witnessed significant advancements in self-supervised learning (SSL) methods for speech-processing tasks. Various speech-based SSL models have been developed and present promising performance on a range of downstream tasks including speech recognition. However, existing speech-based SSL models face a common dilemma in terms of computational cost, which might hinder their potential application and in-depth academic research. To address this issue, we first analyze the computational cost of different modules during HuBERT pre-training and then introduce a stack of efficiency optimizations, which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be trained in 1.1 days with 8 V100 GPUs on the Librispeech 960 h benchmark, without performance degradation, resulting in a 5.2x speedup, compared to the original implementation. Moreover, we explore two well-studied techniques in the Fast-HuBERT and demonstrate consistent improvements as reported in previous work.11The code for Fast-HuBERT training is available at https://***/yanghaha0908/FastHuBERT © 2023 IEEE.

关键词： Speech recognition

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Exploring Time-Frequency Domain Target Speaker Extraction For Causal and Non-Causal Processing

Exploring Time-Frequency Domain Target Speaker Extraction Fo...

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Zhang, Wangyou Yang, Lei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute Department of Computer Science and Engineering Shanghai China China

ISBN: (纸本)9798350306897

In recent years, target speaker extraction (TSE) has drawn increasing interest as an alternative to speech separation in realistic applications. While time-domain methods have been widely used in recent studies to achieve high performance, the potential of time-frequency (T-F) domain methods have been less explored. In this paper, we try to fill this gap and propose to incorporate the top-performing T-F domain speech separation method into the TSE framework. We first explore different speaker information fusion methods for the proposed model. In addition to the commonly-used concatenation based fusion, we propose a novel speaker token-based fusion method to fuse the target speaker information. Second, we show that the proposed model can be easily extended for causal processing with strong performance. Experiments on the WSJ0-2mix and LibriMix benchmarks show that our proposed model outperforms the widely-used time-domain models in both causal and non-causal settings by a large margin. © 2023 IEEE.

关键词： Extraction

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Leying Zhang, Wangyou Chen, Zhengyang Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios. © 2025 IEEE.

关键词： background preservation background removal flow-matching text-to-speech

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Advancing Non-intrusive Suppression on Enhancement Distortion for Noise Robust ASR

Advancing Non-intrusive Suppression on Enhancement Distortio...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Wei Zhao, Siyi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends. © 2025 IEEE.

关键词： non-intrusive robust speech recognition speech distortion speech enhancement

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Robust Audio-Visual ASR with Unified Cross-Modal Attention 48

Robust Audio-Visual ASR with Unified Cross-Modal Attention

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Jiahong Li, Chenda Wu, Yifei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Audio-visual speech recognition (AVSR) takes advantage of noise-invariant visual information to improve the robustness of automatic speech recognition (ASR) systems. While previous works mainly focused on the clean condition, we believe the visual modality is more effective in noisy environments. The challenges arise from the difficulty of adaptive fusion of audio-visual information and the possible interferences inside the training data. In this paper, we present a new audio-visual speech recognition model with a unified cross-modal attention mechanism. In particular, the auxiliary visual evidence is combined with the acoustic feature along the temporal dimension in the unified space before the deep encoding network. This method provides a flexible cross-modal context and requires no forced alignment such that the model can learn to leverage the audio-visual information in relevant frames. In experiments, the proposed model is demonstrated to be robust to the potential absence of the visual modality or misalignment in audio-visual frames. On the large-scale audio-visual dataset LRS3, our new model further reduces the state-of-The-Art WER for clean utterances and significantly improves the performance under noisy conditions. © 2023 IEEE.

关键词： Speech recognition

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Diverse and Vivid Sound Generation from Text Descriptions 48

Diverse and Vivid Sound Generation from Text Descriptions

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Guangwei Xu, Xuenan Dai, Lingfeng Wu, Mengyue Yu, Kai Shanghai Jiao Tong University X-Lance Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai China

ISBN: (纸本)9781728163277

Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results. © 2023 IEEE.

关键词： Music

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Adaptive Large Margin Fine-Tuning For Robust Speaker Verification 48

Adaptive Large Margin Fine-Tuning For Robust Speaker Verific...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Leying Chen, Zhengyang Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Large margin fine-tuning (LMFT) is an effective strategy to improve the speaker verification system's performance and is widely used in speaker verification challenge systems. Because the large margin in the loss function could make the training task too difficult, people usually use longer training segments to alleviate this problem in LMFT. However, the LMFT model could have a duration mismatch with the real scenario verification, where the verification speech may be very short. In our experiments, we also find that LMFT fails in short duration and other verification scenarios. To solve this problem, we propose the duration-based and similarity-based adaptive large margin fine-tuning (ALMFT) strategy. To verify its effectiveness, we constructed fixed, variable length, and asymmetric verification trials based on VoxCeleb1. Experimental results demonstrate that ALMFT algorithms are very effective and robust, which not only achieve comparable improvement with LMFT in official VoxCeleb evaluation trials but also overcome performance degradation problems in short-duration and asymmetric scenarios respectively. © 2023 IEEE.

关键词： asymmetric scenario duration mismatch large margin fine-tuning speaker verification

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