###### Abstract  
Instrument recognition is a crucial aspect of music information retrieval, and in recent years, machine learning-based methods have become the primary approach to addressing this challenge. However, existing models often struggle to accurately identify multiple instruments within music tracks that vary in length and quality. One key issue is that the instruments of interest may not appear in every clip of the audio sample, and when they do, they are often unevenly distributed across different sections of the track. Additionally, in polyphonic music, multiple instruments are often played simultaneously, leading to signal overlap. Using the same overlapping audio signals as partial classification features for different instruments will reduce the distinguishability of features between instruments, thereby affecting the performance of instrument recognition. These complexities present significant challenges for current instrument recognition models. Therefore, this paper proposes a multi-instance multi-scale graph attention neural network (MMGAT) with label semantic embeddings for instrument recognition. MMGAT designs an instance correlation graph to model the presence and quantitative timbre similarity of instruments at different positions from the perspective of multi-instance learning. Then, to enhance the distinguishability of signals after the overlap of different instruments and improve classification accuracy, MMGAT learns semantic information from the labels of different instruments as embeddings and incorporates them into the overlapping audio signal features, thereby enhancing the differentiability of audio features for various instruments. MMGAT then designs an instance-based multi-instance multi-scale graph attention neural network to recognize different instruments based on the instance correlation graphs and label semantic embeddings. The effectiveness of MMGAT is validated through experiments and compared to commonly used instrument recognition models. The experimental results demonstrate that MMGAT outperforms existing approaches in instrument recognition tasks. instrument recognition; multi-instance learning; deep neural network; Graph Neural Network 2025 6 30 2025 6  
classifiers such as Decision Trees or Naive Bayes. However, traditional machine learning methods face significant challenges in achieving high recognition accuracy. They often overlook subtle differences between musical instruments, resulting in suboptimal recognition performance. Moreover, the effectiveness of these methods heavily depends on the manually extracted features, which may not adequately capture the complex and dynamic characteristics of musical instruments [2]. Deep neural networks (DNNs) have emerged as the dominant approach for musical instrument recognition tasks [2; 3], largely due to their ability to model complex audio patterns. However, a significant challenge arises when processing raw signals from recordings of varying quality. This limitation has led to a critical shift towards 2D spectral representations [4], where time-frequency analysis through Convolutional Neural Networks (CNNs) and Transformers marks a substantial performance improvement over traditional methods. Building on this spectral transformation framework, researchers have addressed data scarcity by adopting two complementary strategies: (1) knowledge transfer through pre-training on auxiliary music datasets (such as VGGish, Past, Dy-CNN) [5; 6; 7], and (2) the generation of synthetic data using Wasserstein Generative Adversarial Networks (WGANs) to overcome the constraints posed by low-quality recordings [8; 9]. In addition to these advances, the field has progressed to leverage label semantics, incorporating techniques like multi-instance learning [5; 6] and label augmentation [10]. Multi-task architectures with auxiliary classifiers contribute to a feedback loop, where enhanced feature learning from expanded labels leads to improved recognition, which in turn refines the modeling of label correlations.