###### Abstract
This chain of innovation (from signal representation to data augmentation and semantic enhancement) demonstrates a systematic progression in overcoming the limitations of earlier approaches while building on their strengths. While the machine learning models discussed earlier enhance instrument recognition through effective model design and data (and label) augmentation, they do not fully address the challenges posed by the presence and correlation of multiple instruments within musical pieces of varying lengths. In many cases, the instruments of interest are present only in specific sections of the audio, rather than throughout the entire sample. Furthermore, the instruments that do appear may be scattered across different parts of the music, making it difficult to capture them consistently. Additionally, instruments with similar timbres often share comparable acoustic features, complicating the task of distinguishing them. As a result, it becomes crucial to highlight the audio segments containing the relevant instruments while also weighting instruments with similar timbres for comparison. Moreover, in polyphonic music, where multiple instruments are played simultaneously, their corresponding signals' overlap occurs. Introducing the same overlapping audio signals into the specific features for different instruments will diminish the distinctiveness of their characteristics, ultimately impacting the accuracy of instrument recognition. Based on the above situations, this paper proposes a label correlation-based multi-instance multi-scale graph attention neural network (MMGAT) for instrument recognition. MMGAT converts the one-dimensional sequence of music audio into a two-dimensional graph structure by constructing an instance correlation graph. In this graph, each node represents a time slice of the audio, with its features derived from the extracted time-frequency characteristics. The instance correlation graph forms connections between these nodes by creating edges based on the similarity of their features. To model the label correlation, MMGAT constructs label semantic embeddings by first using the CLIP text encoder to generate semantic vectors for instrument name labels (such as \"piano\" and \"violin\"). Based on this, MMGAT introduces learnable mask vectors into the semantic vectors to decrease noisy label effects. It then builds a multi-layer decoder to calculate the embedding features of the semantic vectors as enhanced features for distinguishing different instruments. Tocapture the timbre similarities of instruments across different positions within the music, MMGAT designs an instance-based multi-scale graph attention mechanism. This mechanism prioritizes instances containing the target instrument and those with instruments of similar timbres by assigning higher attention weights, facilitating instrument recognition through the instance correlation graph and label semantic embeddings within a multi-instance multi-label learning framework for the final instrument classification. To verify the effectiveness of the proposed MMGAT, experiments are conducted using a custom-built music dataset from OpenMIC and IRMAS. The main contributions are listed as follows:  
1. The proposal of an instance correlation graph structure for instrument representation: This graph transforms the sequential linear structure of music audio into the 2-dimensional graph structure to capture the similarity of instances containing similar instruments and the similarity of unrelated instances. 2. The proposal of a multi-instance multi-scale graph attention neural network (MMGAT) with label semantic embeddings based on the instance correlation graphs and label correlations: MMGAT designs a graph attention network with different scales on the instance correlation graphs and a masked label semantic autoencoder for instrument recognition. This paper is structured as follows: Section 2 provides a review of existing approaches for instrument recognition. Section 3 introduces the proposed MMGAT model.