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Shuai Yu

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4 papers

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AAAI Conference 2026 Conference Paper

Every Little Bit Helps: Exploring Better Utilization of Unlabeled Data for Semi-supervised Singing Melody Extraction Using Multi-bands Diffusion Model

Shuai Yu
Xiaoliang He
Kangjie Dong
Yi Yu

Semi-supervised singing melody extraction (SSME) is one of the key tasks in the field of music information retrieval (MIR). Recently, several SSME methods have been proposed and achieved remarkable successes. However, existing methods are still facing two critical issues: firstly, there is a lack of an effective data augmentation method for SSME, which results in insufficient utilization of unlabeled data. Secondly, existing SSME methods discards too much unlabeled data in the stage of consistency regularization, which hinders the further improvements of SSME task. In this paper, we present \emph{ELH-SME}, a novel framework that better utilizes the unlabeled musical data for SSME task. Specifically, our proposed ELH-SME framework consists of three modules: (1) we first propose a diffusion-based multi-bands augmentation (DMA) method to increase the amounts of training data. The proposed DMA methods employs a diffusion model to generate perturbation at the specific frequency bands in an end-to-end manner, thereby avoiding sharply perturbations to the spectrogram. (2) To improve the utilization rate of unlabeled data, we suggest a global-class confidence (GCC) module. During the phase of consistency regularization, we consider both the global-wise and class-wise confidence values, improving the utilization rate of unlabeled data. (3) To further improve the utilization of unlabeled data, we also propose to enhance the representation capability of unlabeled data by extracting channel-level features from labeled data via channel cross attention (CCA). We evaluate our proposed framework on several well-known public available datasets, and the conducted experiments demonstrate the effectiveness of our method.

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ICLR Conference 2025 Conference Paper

ToolACE: Winning the Points of LLM Function Calling

Weiwen Liu
Xu Huang 0008
Xingshan Zeng
Xinlong Hao
Shuai Yu
Dexun Li
Shuai Wang 0020
Weinan Gan

Function calling significantly extends the application boundary of large language models (LLMs), where high-quality and diverse training data is critical for unlocking this capability. However, collecting and annotating real function-calling data is challenging, while synthetic data from existing pipelines often lack coverage and accuracy. In this paper, we present ToolACE, an automatic agentic pipeline designed to generate accurate, complex, and diverse tool-learning data, specifically tailored to the capabilities of LLMs. ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs. Dialogs are further generated through the interplay among multiple agents, under the guidance of a complexity evaluator. To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks. We demonstrate that models trained on our synthesized data---even with only 8B parameters---achieve state-of-the-art performance, comparable to the latest GPT-4 models. Our model and a subset of the data are publicly available at https://huggingface.co/Team-ACE.

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AAAI Conference 2024 Conference Paper

MCSSME: Multi-Task Contrastive Learning for Semi-supervised Singing Melody Extraction from Polyphonic Music

Shuai Yu

Singing melody extraction is an important task in the field of music information retrieval (MIR). The development of data-driven models for this task have achieved great successes. However, the existing models have two major limitations: firstly, most of the existing singing melody extraction models have formulated this task as a pixel-level prediction task. The lack of labeling data has limited the model for further improvements. Secondly, the generalization of the existing models are prone to be disturbed by the music genres. To address the issues mentioned above, in this paper, we propose a multi-Task contrastive learning framework for semi-supervised singing melody extraction, termed as MCSSME. Specifically, to deal with data scarcity limitation, we propose a self-consistency regularization (SCR) method to train the model on the unlabeled data. Transformations are applied to the raw signal of polyphonic music, which makes the network to improve its representation capability via recognizing the transformations. We further propose a novel multi-task learning (MTL) approach to jointly learn singing melody extraction and classification of transformed data. To deal with generalization limitation, we also propose a contrastive embedding learning, which strengthens the intra-class compactness and inter-class separability. To improve the generalization on different music genres, we also propose a domain classification method to learn task-dependent features by mapping data from different music genres to shared subspace. MCSSME evaluates on a set of well-known public melody extraction datasets with promising performances. The experimental results demonstrate the effectiveness of the MCSSME framework for singing melody extraction from polyphonic music using very limited labeled data scenarios.

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AAAI Conference 2019 Short Paper

WAIS: Word Attention for Joint Intent Detection and Slot Filling

Sixuan Chen
Shuai Yu

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved a state-of-the-art performance. Most previous works exploited semantic level information to calculate the attention weights. However, few works have taken the importance of word level information into consideration. In this paper, we propose WAIS, word attention for joint intent detection and slot filling. Considering that intent detection and slot filling have a strong relationship, we further propose a fusion gate that integrates the word level information and semantic level information together for jointly training the two tasks. Extensive experiments show that the proposed model has robust superiority over its competitors and sets the state-of-the-art.

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